Christian Timmerer is a researcher, entrepreneur, and teacher on immersive multimedia communication, streaming, adaptation, and Quality of Experience. He is an Assistant Professor at Alpen-Adria-Universität Klagenfurt, Austria. Follow him on Twitter at http://twitter.com/timse7 and subscribe to his blog at http://blog.timmerer.com.
The 147th MPEG meeting was held in Sapporo, Japan from 15-19 July 2024, and the official press release can be found here. It comprises the following highlights:
ISO Base Media File Format*: The 8th edition was promoted to Final Draft International Standard, supporting seamless media presentation for DASH and CMAF.
Syntactic Description Language: Finalized as an independent standard for MPEG-4 syntax.
Low-Overhead Image File Format*: First milestone achieved for small image handling improvements.
Neural Network Compression*: Second edition for conformance and reference software promoted.
Internet of Media Things (IoMT): Progress made on reference software for distributed media tasks.
* … covered in this column and expanded with possible research aspects.
8th edition of ISO Base Media File Format
The ever-growing expansion of the ISO/IEC 14496-12 ISO base media file format (ISOBMFF) application area has continuously brought new technologies to the standards. During the last couple of years, MPEG Systems (WG 3) has received new technologies on ISOBMFF for more seamless support of ISO/IEC 23009 Dynamic Adaptive Streaming over HTTP (DASH) and ISO/IEC 23000-19 Common Media Application Format (CMAF) leading to the development of the 8th edition of ISO/IEC14496-12.
The new edition of the standard includes new technologies to explicitly indicate the set of tracks representing various versions of the media presentation of a single media for seamless switching and continuous presentation. Such technologies will enable more efficient processing of the ISOBMFF formatted files for DASH manifest or CMAF Fragments.
Research aspects: The central research aspect of the 8th edition of ISOBMFF, which “will enable more efficient processing,” will undoubtedly be its evaluation compared to the state-of-the-art. Standards typically define a format, but how to use it is left open to implementers. Therefore, the implementation is a crucial aspect and will allow for a comparison of performance. One such implementation of ISOBMFF is GPAC, which most likely will be among the first to implement these new features.
Low-Overhead Image File Format
ISO/IEC 23008-12 image format specification defines generic structures for storing image items and sequences based on ISO/IEC 14496-12 ISO base media file format (ISOBMFF). As it allows the use of various high-performance video compression standards for a single image or a series of images, it has been adopted by the market quickly. However, it was challenging to use it for very small-sized images such as icons or emojis. While the initial design of the standard was versatile and useful for a wide range of applications, the size of headers becomes an overhead for applications with tiny images. Thus, Amendment 3 of ISO/IEC 23008-12 low-overhead image file format aims to address this use case by adding a new compact box for storing metadata instead of the ‘Meta’ box to lower the size of the overhead.
Research aspects: The issue regarding header sizes of ISOBMFF for small files or low bitrate (in the case of video streaming) was known for some time. Therefore, amendments in these directions are appreciated while further performance evaluations are needed to confirm design choices made at this initial step of standardization.
Neural Network Compression
An increasing number of artificial intelligence applications based on artificial neural networks, such as edge-based multimedia content processing, content-adaptive video post-processing filters, or federated training, need to exchange updates of neural networks (e.g., after training on additional data or fine-tuning to specific content). For this purpose, MPEG developed a second edition of the standard for coding of neural networks for multimedia content description and analysis (NNC, ISO/IEC 15938-17, published in 2024), adding syntax for differential coding of neural network parameters as well as new coding tools. Trained models can be compressed to at least 10-20% for several architectures, even below 3%, of their original size without performance loss. Higher compression rates are possible at moderate performance degradation. In a distributed training scenario, a model update after a training iteration can be represented at 1% or less of the base model size on average without sacrificing the classification performance of the neural network.
In order to facilitate the implementation of the standard, the accompanying standard ISO/IEC 15938-18 has been updated to cover the second edition of ISO/IEC 15938-17. This standard provides a reference software for encoding and decoding NNC bitstreams, as well as a set of conformance guidelines and reference bitstreams for testing of decoder implementations. The software covers the functionalities of both editions of the standard, and can be configured to test different combinations of coding tools specified by the standard.
Research aspects: The reference software for NNC, together with the reference software for audio/video codecs, are vital tools for building complex multimedia systems and its (baseline) evaluation with respect to compression efficiency only (not speed). This is because reference software is usually designed for functionality (i.e., compression in this case) and not performance.
The 148th MPEG meeting will be held in Kemer, Türkiye, from November 04-08, 2024. Click here for more information about MPEG meetings and their developments.
The 146th MPEG meeting was held in Rennes, France from 22-26 April 2024, and the official press release can be found here. It comprises the following highlights:
AI-based Point Cloud Coding*: Call for proposals focusing on AI-driven point cloud encoding for applications such as immersive experiences and autonomous driving.
Object Wave Compression*: Call for interest in object wave compression for enhancing computer holography transmission.
Open Font Format: Committee Draft of the fifth edition, overcoming previous limitations like the 64K glyph encoding constraint.
Scene Description: Ratified second edition, integrating immersive media objects and extending support for various data types.
MPEG Immersive Video (MIV): New features in the second edition, enhancing the compression of immersive video content.
Video Coding Standards: New editions of AVC, HEVC, and Video CICP, incorporating additional SEI messages and extended multiview profiles.
Machine-Optimized Video Compression*: Advancement in optimizing video encoders for machine analysis.
Video-based Dynamic Mesh Coding (V-DMC)*: Committee Draft status for efficiently storing and transmitting dynamic 3D content.
LiDAR Coding*: Enhanced efficiency and responsiveness in LiDAR data processing with the new standard reaching Committee Draft status.
* … covered in this column.
AI-based Point Cloud Coding
MPEG issued a Call for Proposals (CfP) on AI-based point cloud coding technologies as a result from ongoing explorations regarding use cases, requirements, and the capabilities of AI-driven point cloud encoding, particularly for dynamic point clouds.
With recent significant progress in AI-based point cloud compression technologies, MPEG is keen on studying and adopting AI methodologies. MPEG is specifically looking for learning-based codecs capable of handling a broad spectrum of dynamic point clouds, which are crucial for applications ranging from immersive experiences to autonomous driving and navigation. As the field evolves rapidly, MPEG expects to receive multiple innovative proposals. These may include a unified codec, capable of addressing multiple types of point clouds, or specialized codecs tailored to meet specific requirements, contingent upon demonstrating clear advantages. MPEG has therefore publicly called for submissions of AI-based point cloud codecs, aimed at deepening the understanding of the various options available and their respective impacts. Submissions that meet the requirements outlined in the call will be invited to provide source code for further analysis, potentially laying the groundwork for a new standard in AI-based point cloud coding. MPEG welcomes all relevant contributions and looks forward to evaluating the responses.
Research aspects: In-depth analysis of algorithms, techniques, and methodologies, including a comparative study of various AI-driven point cloud compression techniques to identify the most effective approaches. Other aspects include creating or improving learning-based codecs that can handle dynamic point clouds as well as metrics for evaluating the performance of these codecs in terms of compression efficiency, reconstruction quality, computational complexity, and scalability. Finally, the assessment of how improved point cloud compression can enhance user experiences would be worthwhile to consider here also.
Object Wave Compression
A Call for Interest (CfI) in object wave compression has been issued by MPEG. Computer holography, a 3D display technology, utilizes a digital fringe pattern called a computer-generated hologram (CGH) to reconstruct 3D images from input 3D models. Holographic near-eye displays (HNEDs) reduce the need for extensive pixel counts due to their wearable design, positioning the display near the eye. This positions HNEDs as frontrunners for the early commercialization of computer holography, with significant research underway for product development. Innovative approaches facilitate the transmission of object wave data, crucial for CGH calculations, over networks. Object wave transmission offers several advantages, including independent treatment from playback device optics, lower computational complexity, and compatibility with video coding technology. These advancements open doors for diverse applications, ranging from entertainment experiences to real- time two-way spatial transmissions, revolutionizing fields such as remote surgery and virtual collaboration. As MPEG explores object wave compression for computer holography transmission, a Call for Interest seeks contributions to address market needs in this field.
Research aspects: Apart from compression efficiency, lower computation complexity, and compatibility with video coding technology, there is a range of research aspects, including the design, implementation, and evaluation of coding algorithms within the scope of this CfI. The QoE of computer-generated holograms (CGHs) together with holographic near-eye displays (HNEDs) is yet another dimension to be explored.
Machine-Optimized Video Compression
MPEG started working on a technical report regarding to the “Optimization of Encoders and Receiving Systems for Machine Analysis of Coded Video Content”. In recent years, the efficacy of machine learning-based algorithms in video content analysis has steadily improved. However, an encoder designed for human consumption does not always produce compressed video conducive to effective machine analysis. This challenge lies not in the compression standard but in optimizing the encoder or receiving system. The forthcoming technical report addresses this gap by showcasing technologies and methods that optimize encoders or receiving systems to enhance machine analysis performance.
Research aspects: Video (and audio) coding for machines has been recently addressed by MPEG Video and Audio working groups, respectively. MPEG Joint Video Experts Team with ITU-T SG16, also known as JVET, joined this space with a technical report, but research aspects remain unchanged, i.e., coding efficiency, metrics, and quality aspects for machine analysis of compressed/coded video content.
MPEG-I Immersive Audio
MPEG Audio Coding is entering the “immersive space” with MPEG-I immersive audio and its corresponding reference software. The MPEG-I immersive audio standard sets a new benchmark for compact and lifelike audio representation in virtual and physical spaces, catering to Virtual, Augmented, and Mixed Reality (VR/AR/MR) applications. By enabling high-quality, real-time interactive rendering of audio content with six degrees of freedom (6DoF), users can experience immersion, freely exploring 3D environments while enjoying dynamic audio. Designed in accordance with MPEG’s rigorous standards, MPEG-I immersive audio ensures efficient distribution across bandwidth-constrained networks without compromising on quality. Unlike proprietary frameworks, this standard prioritizes interoperability, stability, and versatility, supporting both streaming and downloadable content while seamlessly integrating with MPEG-H 3D audio compression. MPEG-I’s comprehensive modeling of real-world acoustic effects, including sound source properties and environmental characteristics, guarantees an authentic auditory experience. Moreover, its efficient rendering algorithms balance computational complexity with accuracy, empowering users to finely tune scene characteristics for desired outcomes.
Research aspects: Evaluating QoE of MPEG-I immersive audio-enabled environments as well as the efficient audio distribution across bandwidth-constrained networks without compromising on audio quality are two important research aspects to be addressed by the research community.
Video-based Dynamic Mesh Coding (V-DMC)
Video-based Dynamic Mesh Compression (V-DMC) represents a significant advancement in 3D content compression, catering to the ever-increasing complexity of dynamic meshes used across various applications, including real-time communications, storage, free-viewpoint video, augmented reality (AR), and virtual reality (VR). The standard addresses the challenges associated with dynamic meshes that exhibit time-varying connectivity and attribute maps, which were not sufficiently supported by previous standards. Video-based Dynamic Mesh Compression promises to revolutionize how dynamic 3D content is stored and transmitted, allowing more efficient and realistic interactions with 3D content globally.
Research aspects: V-DMC aims to allow “more efficient and realistic interactions with 3D content”, which are subject to research, i.e., compression efficiency vs. QoE in constrained networked environments.
Low Latency, Low Complexity LiDAR Coding
Low Latency, Low Complexity LiDAR Coding underscores MPEG’s commitment to advancing coding technologies required by modern LiDAR applications across diverse sectors. The new standard addresses critical needs in the processing and compression of LiDAR-acquired point clouds, which are integral to applications ranging from automated driving to smart city management. It provides an optimized solution for scenarios requiring high efficiency in both compression and real-time delivery, responding to the increasingly complex demands of LiDAR data handling. LiDAR technology has become essential for various applications that require detailed environmental scanning, from autonomous vehicles navigating roads to robots mapping indoor spaces. The Low Latency, Low Complexity LiDAR Coding standard will facilitate a new level of efficiency and responsiveness in LiDAR data processing, which is critical for the real-time decision-making capabilities needed in these applications. This standard builds on comprehensive analysis and industry feedback to address specific challenges such as noise reduction, temporal data redundancy, and the need for region-based quality of compression. The standard also emphasizes the importance of low latency coding to support real-time applications, essential for operational safety and efficiency in dynamic environments.
Research aspects: This standard effectively tackles the challenge of balancing high compression efficiency with real-time capabilities, addressing these often conflicting goals. Researchers may carefully consider these aspects and make meaningful contributions.
The 147th MPEG meeting will be held in Sapporo, Japan, from July 15-19, 2024. Click here for more information about MPEG meetings and their developments.
Abstract: Energy efficiency has become a crucial aspect of today’s IT infrastructures, and video (streaming) accounts for over half of today’s Internet traffic. This column highlights open-source tools, datasets, and solutions addressing energy efficiency in video streaming presented at ACM Multimedia Systems 2024 and its co-located workshop ACM Green Multimedia Systems.
Introduction
Across various platforms, users seek the highest Quality of Experience (QoE) in video communication and streaming. Whether it’s a crucial business meeting or a relaxing evening of entertainment, individuals desire seamless and high-quality video experiences. However, meeting this demand for high-quality video comes with a cost: increased energy usage [1],[2]. This energy consumption occurs at every stage of the process, including content provision via cloud services and consumption on end users’ devices [3]. Unfortunately, this heightened energy consumption inevitably leads to higher CO2 emissions (except for renewable energy sources), posing environmental challenges. It emphasizes the need for studies to assess the carbon footprint of video streaming.
Content provision is a critical stage in video streaming, involving encoding videos into various formats, resolutions, and bitrates. Encoding demands computing power and energy, especially in cloud-based systems. Cloud computing has become famous for video encoding due to its scalability [4] to adjust cloud resources to handle changing workloads and flexibility [5] to scale their operations based on demand. However, this convenience comes at a cost. Data centers, the heart of cloud computing, consume a significant portion of global electricity, around 3% [6]. Video encoding is one of the biggest energy consumers within these data centers. Therefore, optimizing video encoding for lower energy consumption is crucial for reducing the environmental impact of cloud-based video delivery.
Content consumption [7] involves the device using the network interface card to request and download video segments from the server, decompressing them for playback, and finally rendering the decoded frames on the screen, where the energy consumption depends on the screen technology and brightness settings.
The GAIA project showcased its research on the environmental impact of video streaming at the recent 15th ACM Multimedia Systems Conference (April 15-18, Bari, Italy). We presented our findings at relevant conference sessions: Open-Source Software and Dataset and the Green Multimedia Systems (GMSys) workshop.
Open Source Software
GREEM: An Open-Source Benchmark Tool Measuring the Environmental Footprint of Video Streaming [PDF] [Github] [Poster]
GREEM (Gaia Resource Energy and Emission Monitoring) aims to measure energy usage during video encoding and decoding processes. GREEM tracks the effects of video processing on hardware performance and provides a suite of analytical scenarios. This tool offers easy-to-use scenarios covering the most common video streaming situations, such as measuring sequential and parallel video encoding and decoding.
Automates experimentation: It allows users to easily configure and run various encoding scenarios with different parameters to compare results.
In-depth monitoring: The tool traces numerous hardware parameters, specifically monitoring energy consumption and GPU metrics, including core and memory utilization, temperature, and fan speed, providing a complete picture of video processing resource usage.
Visualization: GREEM offers scripts that generate analytic plots, allowing users to visualize and understand their measurement results easily.
Verifiable: GREEM empowers researchers with a tool that has earned the ACM Reproducibility Badge, which allows others to reproduce the experiments and results reported in the paper.
Open Source Datasets
VEED: Video Encoding Energy and CO2 Emissions Dataset for AWS EC2 instances [PDF] [Github] [Poster]
As video encoding increasingly shifts to cloud-based services, concerns about the environmental impact of massive data centers arise. The Video Encoding Energy and CO2 Emissions Dataset (VEED) provides the energy consumption and CO2 emissions associated with video encoding on Amazon’s Elastic Compute Cloud (EC2) instances. Additionally, VEED goes beyond energy consumption as it also captures encoding duration and CPU utilization.
Contributions:
Findability: A comprehensive metadata description file ensures VEED’s discoverability for researchers.
Accessibility: VEED is open for download on GitHub (https://github.com/cd-athena/VEEDdataset), removing access barriers for researchers. Core findings in the research that leverages the VEED dataset have been independently verified (ACM Reproducibility Badge).
Interoperability: The dataset is provided in a comma-separated value (CSV) format, allowing integration with various analysis applications.
Reusability: Description files empower researchers to understand the data structure and context, facilitating its use in diverse analytical projects.
COCONUT: Content Consumption Energy Measurement Dataset for Adaptive Video Streaming [PDF] [Github]
COCONUT is a dataset comprising the energy consumption of video streaming across various devices and different HAS (HTTP Adaptive Streaming) players. COCONUT captures user data during MPEG-DASH video segment streaming on laptops, smartphones, and other client devices, measuring energy consumption at different stages of streaming, including segment retrieval through the network interface card, video decoding, and rendering on the device.This paper has been designated the ACM Artifacts Available badge, signifying that the COCONUT dataset is publicly accessible. COCONUT can be accessed at https://athena.itec.aau.at/coconut/.
Second International ACM Green Multimedia Systems Workshop — GMSys 2024
VEEP: Video Encoding Energy and CO2 Emission Prediction [pdf] [slides]
In VEEP, a machine learning (ML) scheme that empowers users to predict the energy consumption and CO2 emissions associated with cloud-based video encoding.
Contributions:
Content-aware energy prediction: VEEP analyzes video content to extract features impacting encoding complexity. This understanding feeds an ML model that accurately predicts the energy consumption required for encoding the video on AWS EC2 instances. (High Accuracy: Achieves an R² score of 0.96)
Real-time carbon footprint: VEEP goes beyond energy. It also factors in real-time carbon intensity data based on the location of the cloud instance. This allows VEEP to calculate the associated CO2 emissions for your encoding tasks at encoding time.
Resulting impact: By carefully selecting the type and location of cloud instances based on VEEP’s predictions, CO2 emissions can be reduced by up to 375 times. This significant reduction signifies VEEP’s potential to contribute to greener video encoding.
Conclusions
This column provided an overview of the GAIA project’s research on the environmental impact of video streaming, presented at the 15th ACM Multimedia Systems Conference. GREEM measurement tool empowers developers and researchers to measure the energy and CO2 emissions of video processing. VEED provides valuable insights into energy consumption and CO2 emissions during cloud-based video encoding on AWS EC2 instances. COCONUT sheds light on energy usage during video playback on various devices and with different players, aiding in optimizing client-side video streaming. Furthermore, VEEP, a machine learning framework, takes energy efficiency a step further. It allows users to predict energy consumption and CO2 emissions associated with cloud-based video encoding, allowing users to select cloud instances that minimize environmental impact. These studies can help researchers, developers, and service providers to optimize video streaming for a more sustainable future. The focus on encoding and playback highlights the importance of a holistic approach considering the entire video streaming lifecycle. While these papers primarily focus on the environmental impact of video streaming, a strong connection exists between energy efficiency and QoE [8],[9],[10]. Optimizing video processing for lower energy consumption can sometimes lead to trade-offs regarding video quality. Future research directions could explore techniques for optimizing video processing while ensuring a consistently high QoE for viewers.
References
[1] A. Katsenou, J. Mao, and I. Mavromatis, “Energy-Rate-Quality Tradeoffs of State-of-the-Art Video Codecs.” arXiv, Oct. 02, 2022. Accessed: Oct. 06, 2022. [Online]. Available: http://arxiv.org/abs/2210.00618
[2] H. Amirpour, V. V. Menon, S. Afzal, R. Prodan, and C. Timmerer, “Optimizing video streaming for sustainability and quality: The role of preset selection in per-title encoding,” in 2023 IEEE International Conference on Multimedia and Expo (ICME), IEEE, 2023, pp. 1679–1684. Accessed: May 05, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10219577/
[4] A. Atadoga, U. J. Umoga, O. A. Lottu, and E. O. Sodiya, “Evaluating the impact of cloud computing on accounting firms: A review of efficiency, scalability, and data security,” Glob. J. Eng. Technol. Adv., vol. 18, no. 2, pp. 065–075, Feb. 2024, doi: 10.30574/gjeta.2024.18.2.0027.
[5] B. Zeng, Y. Zhou, X. Xu, and D. Cai, “Bi-level planning approach for incorporating the demand-side flexibility of cloud data centers under electricity-carbon markets,” Appl. Energy, vol. 357, p. 122406, Mar. 2024, doi: 10.1016/j.apenergy.2023.122406.
[7] C. Yue, S. Sen, B. Wang, Y. Qin, and F. Qian, “Energy considerations for ABR video streaming to smartphones: Measurements, models and insights,” in Proceedings of the 11th ACM Multimedia Systems Conference, 2020, pp. 153–165, doi: 10.1145/3339825.3391867.
[8] G. Bingöl, A. Floris, S. Porcu, C. Timmerer, and L. Atzori, “Are Quality and Sustainability Reconcilable? A Subjective Study on Video QoE, Luminance and Resolution,” in 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2023, pp. 19–24. Accessed: May 06, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10178513/
[9] G. Bingöl, S. Porcu, A. Floris, and L. Atzori, “An Analysis of the Trade-Off Between Sustainability and Quality of Experience for Video Streaming,” in 2023 IEEE International Conference on Communications Workshops (ICC Workshops), IEEE, 2023, pp. 1600–1605. Accessed: May 06, 2024. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/10283614/
[10] C. Herglotz, W. Robitza, A. Raake, T. Hossfeld, and A. Kaup, “Power Reduction Opportunities on End-User Devices in Quality-Steady Video Streaming.” arXiv, May 24, 2023. doi: 10.48550/arXiv.2305.15117.
The 145th MPEG meeting was held online from 22-26 January 2024, and the official press release can be found here. It comprises the following highlights:
Latest Edition of the High Efficiency Image Format Standard Unveils Cutting-Edge Features for Enhanced Image Decoding and Annotation
MPEG Systems finalizes Standards supporting Interoperability Testing
MPEG finalizes the Third Edition of MPEG-D Dynamic Range Control
MPEG finalizes the Second Edition of MPEG-4 Audio Conformance
MPEG Genomic Coding extended to support Transport and File Format for Genomic Annotations
MPEG White Paper: Neural Network Coding (NNC) – Efficient Storage and Inference of Neural Networks for Multimedia Applications
This column will focus on the High Efficiency Image Format (HEIF) and interoperability testing. As usual, a brief update on MPEG-DASH et al. will be provided.
High Efficiency Image Format (HEIF)
The High Efficiency Image Format (HEIF) is a widely adopted standard in the imaging industry that continues to grow in popularity. At the 145th MPEG meeting, MPEG Systems (WG 3) ratified its third edition, which introduces exciting new features, such as progressive decoding capabilities that enhance image quality through a sequential, single-decoder instance process. With this enhancement, users can decode bitstreams in successive steps, with each phase delivering perceptible improvements in image quality compared to the preceding step. Additionally, the new edition introduces a sophisticated data structure that describes the spatial configuration of the camera and outlines the unique characteristics responsible for generating the image content. The update also includes innovative tools for annotating specific areas in diverse shapes, adding a layer of creativity and customization to image content manipulation. These annotation features cater to the diverse needs of users across various industries.
Research aspects: Progressive coding has been a part of modern image coding formats for some time now. However, the inclusion of supplementary metadata provides an opportunity to explore new use cases that can benefit both user experience (UX) and quality of experience (QoE) in academic settings.
Interoperability Testing
MPEG standards typically comprise format definitions (or specifications) to enable interoperability among products and services from different vendors. Interestingly, MPEG goes beyond these format specifications and provides reference software and conformance bitstreams, allowing conformance testing.
At the 145th MPEG meeting, MPEG Systems (WG 3) finalized two standards comprising conformance and reference software by promoting it to the Final Draft International Standard (FDIS), the final stage of standards development. The finalized standards, ISO/IEC 23090-24 and ISO/IEC 23090-25, showcase the pinnacle of conformance and reference software for scene description and visual volumetric video-based coding data, respectively.
ISO/IEC 23090-24 focuses on conformance and reference software for
scene description, providing a comprehensive reference implementation and
bitstream tailored for conformance testing related to ISO/IEC 23090-14, scene
description. This standard opens new avenues for advancements in scene
depiction technologies, setting a new standard for conformance and software
reference in this domain.
Similarly, ISO/IEC 23090-25 targets conformance and reference software for the carriage of visual volumetric video-based coding data. With a dedicated reference implementation and bitstream, this standard is poised to elevate the conformance testing standards for ISO/IEC 23090-10, the carriage of visual volumetric video-based coding data. The introduction of this standard is expected to have a transformative impact on the visualization of volumetric video data.
At the same 145th MPEG meeting, MPEG Audio Coding (WG6) celebrated the completion of the second edition of ISO/IEC 14496-26, audio conformance, elevating it to the Final Draft International Standard (FDIS) stage. This significant update incorporates seven corrigenda and five amendments into the initial edition, originally published in 2010.
ISO/IEC 14496-26 serves as a pivotal standard, providing a framework for designing tests to ensure the compliance of compressed data and decoders with the requirements outlined in ISO/IEC 14496-3 (MPEG-4 Audio). The second edition reflects an evolution of the original, addressing key updates and enhancements through diligent amendments and corrigenda. This latest edition, now at the FDIS stage, marks a notable stride in MPEG Audio Coding’s commitment to refining audio conformance standards and ensuring the seamless integration of compressed data within the MPEG-4 Audio framework.
These standards will be made freely accessible for download on the official ISO website, ensuring widespread availability for industry professionals, researchers, and enthusiasts alike.
Research aspects: Reference software and conformance bitstreams often serve as the basis for further research (and development) activities and, thus, are highly appreciated. For example, reference software of video coding formats (e.g., HM for HEVC, VM for VVC) can be used as a baseline when improving coding efficiency or other aspects of the coding format.
MPEG-DASH Updates
The current status of MPEG-DASH is shown in the figure below.
MPEG-DASH Status, January 2024.
The following most notable aspects have been discussed at the 145th MPEG meeting and adopted into ISO/IEC 23009-1, which will eventually become the 6th edition of the MPEG-DASH standard:
It is now possible to pass CMCD parameters sid and cid via the MPD URL.
Segment duration patterns can be signaled using SegmentTimeline.
Definition of a background mode of operation, which allows a DASH player to receive MPD updates and listen to events without possibly decrypting or rendering any media.
Additionally, the technologies under consideration (TuC) document has been updated with means to signal maximum segment rate, extend copyright license signaling, and improve haptics signaling in DASH. Finally, REAP is progressing towards FDIS but not yet there and most details will be discussed in the upcoming AhG period.
The 146th MPEG meeting will be held in Rennes, France, from April 22-26, 2024. Click here for more information about MPEG meetings and their developments.
The 144th MPEG meeting was held in Hannover, Germany! For those interested, the press release is available with all the details. It’s great to see progress being made in person (cf. also the group pictures below). The main outcome of this meeting is as follows:
MPEG issues Call for Learning-Based Video Codecs for Study of Quality Assessment
MPEG evaluates Call for Proposals on Feature Compression for Video Coding for Machines
MPEG progresses ISOBMFF-related Standards for the Carriage of Network Abstraction Layer Video Data
MPEG enhances the Support of Energy-Efficient Media Consumption
MPEG ratifies the Support of Temporal Scalability for Geometry-based Point Cloud Compression
MPEG reaches the First Milestone for the Interchange of 3D Graphics Formats
MPEG announces Completion of Coding of Genomic Annotations
We have modified the press release to cater to the readers of ACM SIGMM Records and highlighted research on video technologies. This edition of the MPEG column focuses on MPEG Systems-related standards and visual quality assessment. As usual, the column will end with an update on MPEG-DASH.
Attendees of the 144th MPEG meeting in Hannover, Germany.
Visual Quality Assessment
MPEG does not create standards in the visual quality assessment domain. However, it conducts visual quality assessments for its standards during various stages of the standardization process. For instance, it evaluates responses to call for proposals, conducts verification tests of its final standards, and so on. MPEG Visual Quality Assessment (AG 5) issued an open call to study quality assessment for learning-based video codecs. AG 5 has been conducting subjective quality evaluations for coded video content and studying their correlation with objective quality metrics. Most of these studies have focused on the High Efficiency Video Coding (HEVC) and Versatile Video Coding (VVC) standards. To facilitate the study of visual quality, MPEG maintains the Compressed Video for the study of Quality Metrics (CVQM) dataset.
With the recent advancements in learning-based video compression algorithms, MPEG is now studying compression using these codecs. It is expected that reconstructed videos compressed using learning-based codecs will have different types of distortion compared to those induced by traditional block-based motion-compensated video coding designs. To gain a deeper understanding of these distortions and their impact on visual quality, MPEG has issued a public call related to learning-based video codecs. MPEG is open to inputs in response to the call and will invite responses that meet the call’s requirements to submit compressed bitstreams for further study of their subjective quality and potential inclusion into the CVQM dataset.
Considering the rapid advancements in the development of learning-based video compression algorithms, MPEG will keep this call open and anticipates future updates to the call.
Interested parties are kindly requested to contact the MPEG AG 5 Convenor Mathias Wien (wien@lfb.rwth- aachen.de) and submit responses for review at the 145th MPEG meeting in January 2024. Further details are given in the call, issued as AG 5 document N 104 and available from the mpeg.org website.
Research aspects: Learning-based data compression (e.g., for image, audio, video content) is a hot research topic. Research on this topic relies on datasets offering a set of common test sequences, sometimes also common test conditions, that are publicly available and allow for comparison across different schemes. MPEG’s Compressed Video for the study of Quality Metrics (CVQM) dataset is such a dataset, available here, and ready to be used also by researchers and scientists outside of MPEG. The call mentioned above is open for everyone inside/outside of MPEG and allows researchers to participate in international standards efforts (note: to attend meetings, one must become a delegate of a national body).
MPEG Systems-related Standards
At the 144th MPEG meeting, MPEG Systems (WG 3) produced three news-worthy items as follows:
Progression of ISOBMFF-related standards for the carriage of Network Abstraction Layer (NAL) video data.
Enhancement of the support of energy-efficient media consumption.
Support of temporal scalability for geometry-based Point Cloud Compression (PPC).
ISO/IEC 14496-15, a part of the family of ISOBMFF-related standards, defines the carriage of Network Abstract Layer (NAL) unit structured video data such as Advanced Video Coding (AVC), High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC), Essential Video Coding (EVC), and Low Complexity Enhancement Video Coding (LCEVC). This standard has been further improved with the approval of the Final Draft Amendment (FDAM), which adds support for enhanced features such as Picture-in-Picture (PiP) use cases enabled by VVC.
In addition to the improvements made to ISO/IEC 14496-15, separately developed amendments have been consolidated in the 7th edition of the standard. This edition has been promoted to Final Draft International Standard (FDIS), marking the final milestone of the formal standard development.
Another important standard in development is the 2nd edition of ISO/IEC14496-32 (file format reference software and conformance). This standard, currently at the Committee Draft (CD) stage of development, is planned to be completed and reach the status of Final Draft International Standard (FDIS) by the beginning of 2025. This standard will be essential for industry professionals who require a reliable and standardized method of verifying the conformance of their implementation.
MPEG Systems (WG 3) also promoted ISO/IEC 23001-11 (energy-efficient media consumption (green metadata)) Amendment 1 to Final Draft Amendment (FDAM). This amendment introduces energy-efficient media consumption (green metadata) for Essential Video Coding (EVC) and defines metadata that enables a reduction in decoder power consumption. At the same time, ISO/IEC 23001-11 Amendment 2 has been promoted to the Committee Draft Amendment (CDAM) stage of development. This amendment introduces a novel way to carry metadata about display power reduction encoded as a video elementary stream interleaved with the video it describes. The amendment is expected to be completed and reach the status of Final Draft Amendment (FDAM) by the beginning of 2025.
Finally, MPEG Systems (WG 3) promoted ISO/IEC 23090-18 (carriage of geometry-based point cloud compression data) Amendment 1 to Final Draft Amendment (FDAM). This amendment enables the compression of a single elementary stream of point cloud data using ISO/IEC 23090-9 (geometry-based point cloud compression) and storing it in more than one track of ISO Base Media File Format (ISOBMFF)-based files. This enables support for applications that require multiple frame rates within a single file and introduces a track grouping mechanism to indicate multiple tracks carrying a specific temporal layer of a single elementary stream separately.
Research aspects: MPEG Systems usually provides standards on top of existing compression standards, enabling efficient storage and delivery of media data (among others). Researchers may use these standards (including reference software and conformance bitstreams) to conduct research in the general area of multimedia systems (cf. ACM MMSys) or, specifically on green multimedia systems (cf. ACM GMSys).
MPEG-DASH Updates
The current status of MPEG-DASH is shown in the figure below with only minor updates compared to the last meeting.
MPEG-DASH Status, October 2023.
In particular, the 6th edition of MPEG-DASH is scheduled for 2024 but may not include all amendments under development. An overview of existing amendments can be found in the column from the last meeting. Current amendments have been (slightly) updated and progressed toward completion in the upcoming meetings. The signaling of haptics in DASH has been discussed and accepted for inclusion in the Technologies under Consideration (TuC) document. The TuC document comprises candidate technologies for possible future amendments to the MPEG-DASH standard and is publicly available here.
Research aspects: MPEG-DASH has been heavily researched in the multimedia systems, quality, and communications research communities. Adding haptics to MPEG-DASH would provide another dimension worth considering within research, including, but not limited to, performance aspects and Quality of Experience (QoE).
The 145th MPEG meeting will be online from January 22-26, 2024. Click here for more information about MPEG meetings and their developments.
The 143rd MPEG meeting took place in person in Geneva, Switzerland. The official press release can be accessed here and includes the following details:
MPEG finalizes the Carriage of Uncompressed Video and Images in ISOBMFF
MPEG reaches the First Milestone for two ISOBMFF Enhancements
MPEG ratifies Third Editions of VVC and VSEI
MPEG reaches the First Milestone of AVC (11th Edition) and HEVC Amendment
MPEG Genomic Coding extended to support Joint Structured Storage and Transport of Sequencing Data, Annotation Data, and Metadata
MPEG completes Reference Software and Conformance for Geometry-based Point Cloud Compression
We have adjusted the press release to suit the audience of ACM SIGMM and emphasized research on video technologies. This edition of the MPEG column centers around ISOBMFF and video codecs. As always, the column will conclude with an update on MPEG-DASH.
ISOBMFF Enhancements
The ISO Base Media File Format (ISOBMFF) supports the carriage of a wide range of media data such as video, audio, point clouds, haptics, etc., which has now been further extended to uncompressed video and images.
ISO/IEC 23001-17 – Carriage of uncompressed video and images in ISOBMFF – specifies how uncompressed 2D image and video data is carried in files that comply with the ISOBMFF family of standards. This encompasses a range of data types, including monochromatic and colour data, transparency (alpha) information, and depth information. The standard enables the industry to effectively exchange uncompressed video and image data while utilizing all additional information provided by the ISOBMFF, such as timing, color space, and sample aspect ratio for interoperable interpretation and/or display of uncompressed video and image data.
ISO/IEC 14496-15 (based on ISOBMFF) provides the basis for “network abstraction layer (NAL) unit structured video coding formats” such as AVC, HEVC, and VVC. The current version is the 6th edition, which has been amended to support neural-network post-filter supplemental enhancement information (SEI) messages. This amendment defines the carriage of the neural-network post-filter characteristics (NNPFC) SEI messages and the neural-network post-filter activation (NNPFA) SEI messages to enable the delivery of (i) a base post-processing filter and (ii) a series of neural network updates synchronized with the input video pictures/frames.
Research aspects: While the former, the carriage of uncompressed video and images in ISOBMFF, seems to be something obvious to be supported within a file format, the latter enables to use neural network-based post-processing filters to enhance video quality after the decoding process, which is an active field of research. The current extensions with the file format provide a baseline for the evaluation (cf. also next section).
Video Codec Enhancements
MPEG finalized the specifications of the third editions of the Versatile Video Coding (VVC, ISO/IEC 23090-3) and the Versatile Supplemental Enhancement Information (VSEI, ISO/IEC 23002-7) standards. Additionally, MPEG issued the Committee Draft (CD) text of the eleventh edition of the Advanced Video Coding (AVC, ISO/IEC 14496-10) standard and the Committee Draft Amendment (CDAM) text on top of the High Efficiency Video Coding standard (HEVC, ISO/IEC 23008-2).
These SEI messages include two systems-related SEI messages, (a) one for signaling of green metadata as specified in ISO/IEC 23001-11 and (b) the other for signaling of an alternative video decoding interface for immersive media as specified in ISO/IEC 23090-13. Furthermore, the neural network post-filter characteristics SEI message and the neural-network post-processing filter activation SEI message have been added to AVC, HEVC, and VVC.
The two SEI messages for describing and activating post-filters using neural network technology in video bitstreams could, for example, be used for reducing coding noise, spatial and temporal upsampling (i.e., super-resolution and frame interpolation), color improvement, or general denoising of the decoder output. The description of the neural network architecture itself is based on MPEG’s neural network representation standard (ISO/IEC 15938 17). As results from an exploration experiment have shown, neural network-based post-filters can deliver better results than conventional filtering methods. Processes for invoking these new post-filters have already been tested in a software framework and will be made available in an upcoming version of the VVC reference software (ISO/IEC 23090-16).
Research aspects: SEI messages for neural network post-filters (NNPF) for AVC, HEVC, and VVC, including systems supports within the ISOBMFF, is a powerful tool(box) for interoperable visual quality enhancements at the client. This tool(box) will (i) allow for Quality of Experience (QoE) assessments and (ii) enable the analysis thereof across codecs once integrated within the corresponding reference software.
MPEG-DASH Updates
The current status of MPEG-DASH is depicted in the figure below:
The latest edition of MPEG-DASH is the 5th edition (ISO/IEC 23009-1:2022) which is publicly/freely available here. There are currently three amendments under development:
ISO/IEC 23009-1:2022 Amendment 1: Preroll, nonlinear playback, and other extensions. This amendment has been ratified already and is currently being integrated into the 5th edition of part 1 of the MPEG-DASH specification.
ISO/IEC 23009-1:2022 Amendment 2: EDRAP streaming and other extensions. EDRAP stands for Extended Dependent Random Access Point and at this meeting the Draft Amendment (DAM) has been approved. EDRAP increases the coding efficiency for random access and has been adopted within VVC.
ISO/IEC 23009-1:2022 Amendment 3: Segment sequences for random access and switching. This amendment is at Committee Draft Amendment (CDAM) stage, the first milestone of the formal standardization process. This amendment aims at improving tune-in time for low latency streaming.
Additionally, MPEG Technologies under Consideration (TuC) comprises a few new work items, such as content selection and adaptation logic based on device orientation and signalling of haptics data within DASH.
Finally, part 9 of MPEG-DASH — redundant encoding and packaging for segmented live media (REAP) — has been promoted to Draft International Standard (DIS). It is expected to be finalized in the upcoming meetings.
Research aspects: Random access has been extensively evaluated in the context of video coding but not (low latency) streaming. Additionally, the TuC item related to content selection and adaptation logic based on device orientation raises QoE issues to be further explored.
The 144th MPEG meeting will be held in Hannover from October 16-20, 2023. Click here for more information about MPEG meetings and their developments.
The 142nd MPEG meeting was held as a face-to-face meeting in Antalya, Türkiye, and the official press release can be found here and comprises the following items:
MPEG issues Call for Proposals for Feature Coding for Machines
MPEG finalizes the 9th Edition of MPEG-2 Systems
MPEG reaches the First Milestone for Storage and Delivery of Haptics Data
MPEG completes 2nd Edition of Neural Network Coding (NNC)
MPEG completes Verification Test Report and Conformance and Reference Software for MPEG Immersive Video
MPEG finalizes work on metadata-based MPEG-D DRC Loudness Leveling
The press release text has been modified to match the target audience of ACM SIGMM and highlight research aspects targeting researchers in video technologies. This column focuses on the 9th edition of MPEG-2 Systems, storage and delivery of haptics data, neural network coding (NNC), MPEG immersive video (MIV), and updates on MPEG-DASH.
Feature Coding for Video Coding for Machines (FCVCM)
At the 142nd MPEG meeting, MPEG Technical Requirements (WG 2) issued a Call for Proposals (CfP) for technologies and solutions enabling efficient feature compression for video coding for machine vision tasks. This work on “Feature Coding for Video Coding for Machines (FCVCM)” aims at compressing intermediate features within neural networks for machine tasks. As applications for neural networks become more prevalent and the neural networks increase in complexity, use cases such as computational offload become more relevant to facilitate the widespread deployment of applications utilizing such networks. Initially as part of the “Video Coding for Machines” activity, over the last four years, MPEG has investigated potential technologies for efficient compression of feature data encountered within neural networks. This activity has resulted in establishing a set of ‘feature anchors’ that demonstrate the achievable performance for compressing feature data using state-of-the-art standardized technology. These feature anchors include tasks performed on four datasets.
Research aspects: FCVCM is about compression, and the central research aspect here is compression efficiency which can be tested against a commonly agreed dataset (anchors). Additionally, it might be attractive to research which features are relevant for video coding for machines (VCM) and quality metrics in this emerging domain. One might wonder whether, in the future, robots or other AI systems will participate in subjective quality assessments.
9th Edition of MPEG-2 Systems
MPEG-2 Systems was first standardized in 1994, defining two container formats: program stream (e.g., used for DVDs) and transport stream. The latter, also known as MPEG-2 Transport Stream (M2TS), is used for broadcast and internet TV applications and services. MPEG-2 Systems has been awarded a Technology and Engineering Emmy® in 2013 and at the 142nd MPEG meeting, MPEG Systems (WG 3) ratified the 9th edition of ISO/IEC 13818-1 MPEG-2 Systems. The new edition includes support for Low Complexity Enhancement Video Coding (LCEVC), the youngest in the MPEG family of video coding standards on top of more than 50 media stream types, including, but not limited to, 3D Audio and Versatile Video Coding (VVC). The new edition also supports new options for signaling different kinds of media, which can aid the selection of the best audio or other media tracks for specific purposes or user preferences. As an example, it can indicate that a media track provides information about a current emergency.
Research aspects: MPEG container formats such as MPEG-2 Systems and ISO Base Media File Format are necessary for storing and delivering multimedia content but are often neglected in research. Thus, I would like to take up the cudgels on behalf of the MPEG Systems working group and argue that researchers should pay more attention to these container formats and conduct research and experiments for its efficient use with respect to multimedia storage and delivery.
Storage and Delivery of Haptics Data
At the 142nd MPEG meeting, MPEG Systems (WG 3) reached the first milestone for ISO/IEC 23090-32 entitled “Carriage of haptics data” by promoting the text to Committee Draft (CD) status. This specification enables the storage and delivery of haptics data (defined by ISO/IEC 23090-31) in the ISO Base Media File Format (ISOBMFF; ISO/IEC 14496-12). Considering the nature of haptics data composed of spatial and temporal components, a data unit with various spatial or temporal data packets is used as a basic entity like an access unit of audio-visual media. Additionally, an explicit indication of a silent period considering the sparse nature of haptics data has been introduced in this draft. The standard is planned to be completed, i.e., to reach the status of Final Draft International Standard (FDIS), by the end of 2024.
Research aspects: Coding (ISO/IEC 23090-31) and carriage (ISO/IEC 23090-32) of haptics data goes hand in hand and needs further investigation concerning compression efficiency and storage/delivery performance with respect to various use cases.
Neural Network Coding (NNC)
Many applications of artificial neural networks for multimedia analysis and processing (e.g., visual and acoustic classification, extraction of multimedia descriptors, or image and video coding) utilize edge-based content processing or federated training. The trained neural networks for these applications contain many parameters (weights), resulting in a considerable size. Therefore, the MPEG standard for the compressed representation of neural networks for multimedia content description and analysis (NNC, ISO/IEC 15938-17, published in 2022) was developed, which provides a broad set of technologies for parameter reduction and quantization to compress entire neural networks efficiently.
Recently, an increasing number of artificial intelligence applications, such as edge-based content processing, content-adaptive video post-processing filters, or federated training, need to exchange updates of neural networks (e.g., after training on additional data or fine-tuning to specific content). Such updates include changes in the neural network parameters but may also involve structural changes in the neural network (e.g. when extending a classification method with a new class). In scenarios like federated training, these updates must be exchanged frequently, such that much more bandwidth over time is required, e.g., in contrast to the initial deployment of trained neural networks.
The second edition of NNC addresses these applications through efficient representation and coding of incremental updates and extending the set of compression tools that can be applied to both entire neural networks and updates. Trained models can be compressed to at least 10-20% and, for several architectures, even below 3% of their original size without performance loss. Higher compression rates are possible at moderate performance degradation. In a distributed training scenario, a model update after a training iteration can be represented at 1% or less of the base model size on average without sacrificing the classification performance of the neural network. NNC also provides synchronization mechanisms, particularly for distributed artificial intelligence scenarios, e.g., if clients in a federated learning environment drop out and later rejoin.
Research aspects: The incremental compression of neural networks enables various new use cases, which provides research opportunities for media coding and communication, including optimization thereof.
MPEG Immersive Video
At the 142nd MPEG meeting, MPEG Video Coding (WG 4) issued the verification test report of ISO/IEC 23090-12 MPEG immersive video (MIV) and completed the development of the conformance and reference software for MIV (ISO/IEC 23090-23), promoting it to the Final Draft International Standard (FDIS) stage.
MIV was developed to support the compression of immersive video content, in which multiple real or virtual cameras capture a real or virtual 3D scene. The standard enables the storage and distribution of immersive video content over existing and future networks for playback with 6 degrees of freedom (6DoF) of view position and orientation. MIV is a flexible standard for multi-view video plus depth (MVD) and multi-planar video (MPI) that leverages strong hardware support for commonly used video formats to compress volumetric video.
ISO/IEC 23090-23 specifies how to conduct conformance tests and provides reference encoder and decoder software for MIV. This draft includes 23 verified and validated conformance bitstreams spanning all profiles and encoding and decoding reference software based on version 15.1.1 of the test model for MPEG immersive video (TMIV). The test model, objective metrics, and other tools are publicly available at https://gitlab.com/mpeg-i-visual.
Research aspects: Conformance and reference software are usually provided to facilitate product conformance testing, but it also provides researchers with a common platform and dataset, allowing for the reproducibility of their research efforts. Luckily, conformance and reference software are typically publicly available with an appropriate open-source license.
MPEG-DASH Updates
Finally, I’d like to provide a quick update regarding MPEG-DASH, which has become a new part, namely redundant encoding and packaging for segmented live media (REAP; ISO/IEC 23009-9). The following figure provides the reference workflow for redundant encoding and packaging of live segmented media.
Reference workflow for redundant encoding and packaging of live segmented media.
The reference workflow comprises (i) Ingest Media Presentation Description (I-MPD), (ii) Distribution Media Presentation Description (D-MPD), and (iii) Storage Media Presentation Description (S-MPD), among others; each defining constraints on the MPD and tracks of ISO base media file format (ISOBMFF).
Additionally, the MPEG-DASH Break out Group discussed various technologies under consideration, such as (a) combining HTTP GET requests, (b) signaling common media client data (CMCD) and common media server data (CMSD) in a MPEG-DASH MPD, (c) image and video overlays in DASH, and (d) updates on lower latency.
An updated overview of DASH standards/features can be found in the Figure below.
Research aspects: The REAP committee draft (CD) is publicly available feedback from academia and industry is appreciated. In particular, first performance evaluations or/and reports from proof of concept implementations/deployments would be insightful for the next steps in the standardization of REAP.
The 143rd MPEG meeting will be held in Geneva from July 17-21, 2023. Click here for more information about MPEG meetings and their developments.
After several years of online meetings, the 140th MPEG meeting was held as a face-to-face meeting in Mainz, Germany, and the official press release can be found here and comprises the following items:
MPEG evaluates the Call for Proposals on Video Coding for Machines
MPEG evaluates Call for Evidence on Video Coding for Machines Feature Coding
MPEG reaches the First Milestone for Haptics Coding
MPEG completes a New Standard for Video Decoding Interface for Immersive Media
MPEG completes Development of Conformance and Reference Software for Compression of Neural Networks
MPEG White Papers: (i)MPEG-H 3D Audio, (ii)MPEG-I Scene Description
Video Coding for Machines
Video coding is the process of compression and decompression of digital video content with the primary purpose of consumption by humans (e.g., watching a movie or video telephony). Recently, however, massive video data is more and more analyzed without human intervention leading to a new paradigm referred to as Video Coding for Machines (VCM) which targets both (i) conventional video coding and (ii) feature coding (see here for further details).
At the 140th MPEG meeting, MPEG Technical Requirements (WG 2) evaluated the responses to the Call for Proposals (CfP) for technologies and solutions enabling efficient video coding for machine vision tasks. A total of 17 responses to this CfP were received, with responses providing various technologies such as (i) learning-based video codecs, (ii) block-based video codecs, (iii) hybrid solutions combining (i) and (ii), and (iv) novel video coding architectures. Several proposals use a region of interest-based approach, where different areas of the frames are coded in varying qualities.
The responses to the CfP reported an improvement in compression efficiency of up to 57% on object tracking, up to 45% on instance segmentation, and up to 39% on object detection, respectively, in terms of bit rate reduction for equivalent task performance. Notably, all requirements defined by WG 2 were addressed by various proposals.
Furthermore, MPEG Technical Requirements (WG 2) evaluated the responses to the Call for Evidence (CfE) for technologies and solutions enabling efficient feature coding for machine vision tasks. A total of eight responses to this CfE were received, of which six responses were considered valid based on the conditions described in the call:
For the tested video dataset increases in compression efficiency of up to 87% compared to the video anchor and over 90% compared to the feature anchor were reported.
For the tested image dataset, the compression efficiency can be increased by over 90% compared to both image and feature anchors.
Research aspects: the main research area is still the same as described in my last column, i.e., compression efficiency (incl. probably runtime, sometimes called complexity) and Quality of Experience (QoE). Additional research aspects are related to the actual task for which video coding for machines is used (e.g., segmentation, object detection, as mentioned above).
Video Decoding Interface for Immersive Media
One of the most distinctive features of immersive media compared to 2D media is that only a tiny portion of the content is presented to the user. Such a portion is interactively selected at the time of consumption. For example, a user may not see the same point cloud object’s front and back sides simultaneously. Thus, for efficiency reasons and depending on the users’ viewpoint, only the front or back sides need to be delivered, decoded, and presented. Similarly, parts of the scene behind the observer may not need to be accessed.
At the 140th MPEG meeting, MPEG Systems (WG 3) reached the final milestone of the Video Decoding Interface for Immersive Media (VDI) standard (ISO/IEC 23090-13) by promoting the text to Final Draft International Standard (FDIS). The standard defines the basic framework and specific implementation of this framework for various video coding standards, including support for application programming interface (API) standards that are widely used in practice, e.g., Vulkan by Khronos.
The VDI standard allows for dynamic adaptation of video bitstreams to provide the decoded output pictures so that the number of actual video decoders can be smaller than the number of elementary video streams to be decoded. In other cases, virtual instances of video decoders can be associated with the portions of elementary streams required to be decoded. With this standard, the resource requirements of a platform running multiple virtual video decoder instances can be further optimized by considering the specific decoded video regions to be presented to the users rather than considering only the number of video elementary streams in use. The first edition of the VDI standard includes support for the following video coding standards: High Efficiency Video Coding (HEVC), Versatile Video Coding (VVC), and Essential Video Coding (EVC).
Research aspect: VDI is also a promising standard to enable the implementation of viewport adaptive tile-based 360-degree video streaming, but its performance still needs to be assessed in various scenarios. However, requesting and decoding individual tiles within a 360-degree video streaming application is a prerequisite for enabling efficiency in such cases, and VDI provides the basis for its implementation.
MPEG-DASH Updates
Finally, I’d like to provide a quick update regarding MPEG-DASH, which seems to be in maintenance mode. As mentioned in my last blog post, amendments, Defects under Investigation (DuI), and Technologies under Consideration (TuC) are output documents, as well as a new working draft called Redundant encoding and packaging for segmented live media (REAP), which eventually will become ISO/IEC 23009-9. The scope of REAP is to define media formats for redundant encoding and packaging of live segmented media, media ingest, and asset storage. The current working draft can be downloaded here.
Research aspects: REAP defines a distributed system and, thus, all research aspects related to such systems apply here, e.g., performance and scalability, just to name a few.
The 141st MPEG meeting will be online from January 16-20, 2023. Click here for more information about MPEG meetings and their developments.
Regarding the Intergovernmental Panel on Climate Change (IPCC) report in 2021 and Sustainable Development Goal (SDG) 13 “climate action”, urgent action is needed against climate change and global greenhouse gas (GHG) emissions in the next few years [1]. This urgency also applies to the energy consumption of digital technologies. Internet data traffic is responsible for more than half of digital technology’s global impact, which is 55% of energy consumption annually. The Shift Project forecast [2] shows an increase of 25% in data traffic associated with 9% more energy consumption per year, reaching 8% of all GHG emissions in 2025.
Video flows represented 80% of global data flows in 2018, and this video data volume is increasing by 80% annually [2]. This exponential increase in the use of streaming video is due to (i) improvements in Internet connections and service offerings [3], (ii) the rapid development of video entertainment (e.g., video games and cloud gaming services), (iii) the deployment of Ultra High-Definition (UHD, 4K, 8K), Virtual Reality (VR), and Augmented Reality (AR), and (iv) an increasing number of video surveillance and IoT applications [4]. Interestingly, video processing and streaming generate 306 million tons of CO2, which is 20% of digital technology’s total GHG emissions and nearly 1% of worldwide GHG emissions [2].
While research has shown that the carbon footprint of video streaming has been decreasing in recent years [5], there is still a high need to invest in research and development of efficient next-generation computing and communication technologies for video processing technologies. This carbon footprint reduction is due to technology efficiency trends in cloud computing (e.g., renewable power), emerging modern mobile networks (e.g., growth in Internet speed), and end-user devices (e.g., users prefer less energy-intensive mobile and tablet devices over larger PCs and laptops). However, since the demand for video streaming is growing dramatically, it raises the risk of increased energy consumption.
Investigating energy efficiency during video streaming is essential to developing sustainable video technologies. The processes from video encoding to decoding and displaying the video on the end user’s screen require electricity, which results in CO2 emissions. Consequently, the key question becomes: “How can we improve energy efficiency for video streaming systems while maintaining an acceptable Quality of Experience (QoE)?”.
Challenges and Opportunities
In this section, we will outline challenges and opportunities to tackle the associated emissions for video streaming of (i) data centers, (ii) networks, and (iii) end-user devices [5] – presented in Figure 1.
Figure 1. Challenges and opportunities to tackle emissions for video streaming.
Data centers are responsible for the video encoding process and storage of the video content. The video data traffic volume grows through data centers, driving their workloads with the estimated total power consumption of more than 1,000 TWh by 2025 [6]. Data centers are the most prioritized target of regulatory initiatives. National and regional policies are established related to the growing number of data centers and the concern over their energy consumption [7].
Suitable cloud services: Select energy-optimized and sustainable cloud services to help reduce CO2 emissions. Recently, IT service providers have started innovating in energy-efficient hardware by designing highly efficient Tensor Processing Units, high-performance servers, and machine-learning approaches to optimize cooling automatically to reduce the energy consumption in their data centers [8]. In addition to advances in hardware designs, it is also essential to consider the software’s potential for improvements in energy efficiency [9].
Low-carbon cloud regions: IT service providers offer cloud computing platforms in multiple regions delivered through a global network of data centers. Various power plants (e.g., fuel, natural gas, coal, wind, sun, and water) supply electricity to run these data centers generating different amounts of greenhouse gases. Therefore, it is essential to consider how much carbon is emitted by the power plants that generate electricity to run cloud services in the selected region for cloud computing. Thus, a cloud region needs to be considered by its entire carbon footprint, including its source of energy production.
Efficient and fast transcoders (and encoders): Another essential factor to be considered is using efficient transcoders/encoders that can transcode/encode the video content faster and with less energy consumption but still at an acceptable quality for the end-user [10][11][12].
Optimizing the video encoding parameters: There is a huge potential in optimizing the overall energy consumption of video streaming by optimizing the video encoding parameters to reduce the bitrates of encoded videos without affecting quality, including choosing a more power-efficient codec, resolution, frame rate, and bitrate among other parameters.
The next component within the video streaming process is video delivery within heterogeneous networks. Two essential energy consumption factors for video delivery are the network technology used and the amount of data to be transferred.
Energy-efficient network technology for video streaming: the network technology used to transmit data from the data center to the end-users determine energy performance since the networks’ GHG emissions vary widely [5]. A fiber-optic network is the most climate-friendly transmission technology, with only 2 grams of CO2 per hour of HD video streaming, while a copper cable (VDSL) generates twice as much (i.e., 4 grams of CO2 per hour). UMTS data transmission (3G) produces 90 grams of CO2 per hour, reduced to 5 grams of CO2 per hour when using 5G [13]. Therefore, research shows that expanding fiber-optic networks and 5G transmission technology are promising for climate change mitigation [5].
Lower data transmission: Lower data transmission drops energy consumption. Therefore, the amount of video data needs to be reduced without compromising video quality [2]. The video data per hour for various resolutions and qualities range from 30 MB/hr for very low resolutions to 7 GB/hr for UHD resolutions. A higher data volume causes more transmission energy. Another possibility is the reduction of unnecessary video usage, for example, by avoiding autoplay and embedded videos. Such video content aims to maximize the quantity of content consumed. Broadcasting platforms also play a central role in how viewers consume content and, thus, the impact on the environment [2].
The last component of the video streaming process is video usage at the end-user device, including decoding and displaying the video content on the end-user devices like personal computers, laptops, tablets, phones, or television sets.
End-user devices: Research works [3][14] show that the end-user devices and decoding hardware account for the greatest portion of energy consumption and CO2 emission in video streaming. Thus, most reduction strategies lay within the energy efficiency of the end-user devices, for instance, by improving screen display technologies or shifting from desktops to using more energy-efficient laptops, tablets, and smartphones.
Streaming parameters: Energy consumption of the video decoding process depends on video streaming parameters similar to the end-user QoE. Thus, it is important to intelligently select video streaming parameters to optimize the QoE and power efficiency of the end-user device. Moreover, different underlying video encoding parameters also impact the video decodings’ energy usage.
End-user device environment: A wide variety of browsers (including legacy versions), codecs, and operating systems besides the hardware (e.g., CPU, display) determine the final power consumption.
In this column, we argue that these challenges and opportunities for green video streaming can help to gain insights that further drive the adoption of novel, more sustainable usage patterns to reduce the overall energy consumption of video streaming without sacrificing end-user’s QoE.
End-to-end video streaming: While we have highlighted the main factors of each video streaming component that impact energy consumption to create a generic power consumption model, we need to study and holistically analyze video streaming and its impact on all components. Implementing a dedicated system for optimizing energy consumption may introduce additional processing on top of regular service operations if not done efficiently.For instance, overall traffic will be reduced when using the most recent video codec (e.g., VVC) compared to AVC (the most deployed video codec up to date), but its encoding and decoding complexity will be increased and, thus, require more energy.
Optimizing the video streaming parameters: There is a huge potential in optimizing the overall energy consumption for video service providers by optimizing the video streaming parameters, including choosing a more power-efficient codec implementation, resolution, frame rate, and bitrate, among other parameters.
GAIA: Intelligent Climate-Friendly Video Platform
Recently, we started the “GAIA” project to research the aspects mentioned before. In particular, the GAIA project researches and develops a climate-friendly adaptive video streaming platform that provides (i) complete energy awareness and accountability, including energy consumption and GHG emissions along the entire delivery chain, from content creation and server-side encoding to video transmission and client-side rendering; and (ii) reduced energy consumption and GHG emissions through advanced analytics and optimizations on all phases of the video delivery chain.
Figure 2. GAIA high-level approach for the intelligent climate-friendly video platform.
As shown in Figure 2, the research considered in GAIA comprises benchmarking, energy-aware and machine learning-based modeling, optimization algorithms, monitoring, and auto-tuning.
Energy-aware benchmarking involves a functional requirement analysis of the leading project objectives, measurement of the energy for transcoding video tasks on various heterogeneous cloud and edge resources, video delivery, and video decoding on end-user devices.
Energy-aware modelling and prediction use the benchmarking results and the data collected from real deployments to build regression and machine learning. The models predict the energy consumed by heterogeneous cloud and edge resources, possibly distributed across various clouds and delivery networks. We further provide energy models for video distribution on different channels and consider the relation between bitrate, codec, and video quality.
Energy-aware optimization and scheduling researches and develops appropriate generic algorithms according to the requirements for real-time delivery (including encoding and transmission) of video processing tasks (i.e., transcoding) deployed on heterogeneous cloud and edge infrastructures.
Energy-aware monitoring and auto-tuning perform dynamic real-time energy monitoring of the different video delivery chains for improved data collection, benchmarking, modelling and optimization.
GMSys 2023: First International ACM Green Multimedia Systems Workshop
Finally, we would like to use this opportunity to highlight and promote the first International ACM Green Multimedia Systems Workshop (GMSys’23). The GMSys’23 takes place in Vancouver, Canada, in June 2023 co-located with ACM Multimedia Systems 2023. We expect a series of at least three consecutive workshops since this topic may critically impact the innovation and development of climate-effective approaches. This workshop strongly focuses on recent developments and challenges for energy reduction in multimedia systems and the innovations, concepts, and energy-efficient solutions from video generation to processing, delivery, and consumption. Please see the Call for Papers for further details.
Final Remarks
In both the GAIA project and ACM GMSys workshop, there are various actions and initiatives to put energy efficiency-related topics for video streaming on the center stage of research and development. In this column, we highlighted major video streaming components concerning their possible challenges and opportunities enabling energy-efficient, sustainable video streaming, sometimes also referred to as green video streaming. Having a thorough understanding of the key issues and gaining meaningful insights are essential for successful research.
References
[1] IPCC, 2021: Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change[Masson-Delmotte, V., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, In press, doi:10.1017/9781009157896. [2] M. Efoui-Hess, Climate Crisis: the unsustainable use of online video – The practical case for digital sobriety, Technical Report, The Shift Project, July, 2019. [3] IEA (2020), The carbon footprint of streaming video: fact-checking the headlines, IEA, Paris https://www.iea.org/commentaries/the-carbon-footprint-of-streaming-video-fact-checking-the-headlines. [4] Cisco Annual Internet Report (2018–2023) White Paper, 2018 (updated 2020), https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html. [5] C. Fletcher, et al., Carbon impact of video streaming, Technical Report, 2021, https://s22.q4cdn.com/959853165/files/doc_events/2021/Carbon-impact-of-video-streaming.pdf. [6] Huawei Releases Top 10 Trends of Data Center Facility in 2025, 2020, https://www.huawei.com/en/news/2020/2/huawei-top10-trends-datacenter-facility-2025. [7] COMMISSION REGULATION (EC) No 642/2009, Official Journal of the European Union, 2009, https://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2009:191:0042:0052:EN:PDF#:~:text=COMMISSION%20REGULATION%20(EC)%20No%20642/2009%20of%2022%20July,regard%20to%20the%20Treaty%20establishing%20the%20European%20Community. [8] U. Hölzle, Data centers are more energy efficient than ever, Technical Report, 2020, https://blog.google/outreach-initiatives/sustainability/data-centers-energy-efficient/. [9] Charles E. Leiserson, Neil C. Thompson, Joel S. Emer, Bradley C. Kuszmaul, Butler W. Lampson, Daniel Sanchez, and Tao B. Schardl. 2020. There’s plenty of room at the Top: What will drive computer performance after Moore’s law? Science 368, 6495 (2020), eaam9744. DOI:https://doi.org/10.1126/science.aam9744 [10] M. G. Koziri, P. K. Papadopoulos, N. Tziritas, T. Loukopoulos, S. U. Khan and A. Y. Zomaya, “Efficient Cloud Provisioning for Video Transcoding: Review, Open Challenges and Future Opportunities,” in IEEE Internet Computing, vol. 22, no. 5, pp. 46-55, Sep./Oct. 2018, doi: 10.1109/MIC.2017.3301630. [11] J. -F. Franche and S. Coulombe, “Fast H.264 to HEVC transcoder based on post-order traversal of quadtree structure,” 2015 IEEE International Conference on Image Processing (ICIP), Quebec City, QC, Canada, 2015, pp. 477-481, doi: 10.1109/ICIP.2015.7350844. [12] E. de la Torre, R. Rodriguez-Sanchez and J. L. Martínez, “Fast video transcoding from HEVC to VP9,” in IEEE Transactions on Consumer Electronics, vol. 61, no. 3, pp. 336-343, Aug. 2015, doi: 10.1109/TCE.2015.7298293. [13] Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, Video streaming: data transmission technology crucial for climate footprint, No. 144/20, 2020, https://www.bmuv.de/en/pressrelease/video-streaming-data-transmission-technology-crucial-for-climate-footprint/ [14] Malmodin, Jens, and Dag Lundén. 2018. “The Energy and Carbon Footprint of the Global ICT and E&M Sectors 2010–2015” Sustainability 10, no. 9: 3027. https://doi.org/10.3390/su10093027
The original blog post can be found at the Bitmovin Techblog and has been modified/updated here to focus on and highlight research aspects.
The 139th MPEG meeting was once again held as an online meeting, and the official press release can be found here and comprises the following items:
MPEG Issues Call for Evidence for Video Coding for Machines (VCM)
MPEG Ratifies the Third Edition of Green Metadata, a Standard for Energy-Efficient Media Consumption
MPEG Completes the Third Edition of the Common Media Application Format (CMAF) by adding Support for 8K and High Frame Rate for High Efficiency Video Coding
MPEG Scene Descriptions adds Support for Immersive Media Codecs
MPEG Starts New Amendment of VSEI containing Technology for Neural Network-based Post Filtering
MPEG Starts New Edition of Video Coding-Independent Code Points Standard
MPEG White Paper on the Third Edition of the Common Media Application Format
In this report, I’d like to focus on VCM, Green Metadata, CMAF, VSEI, and a brief update about DASH (as usual).
Video Coding for Machines (VCM)
MPEG’s exploration work on Video Coding for Machines (VCM) aims at compressing features for machine-performed tasks such as video object detection and event analysis. As neural networks increase in complexity, architectures such as collaborative intelligence, whereby a network is distributed across an edge device and the cloud, become advantageous. With the rise of newer network architectures being deployed amongst a heterogenous population of edge devices, such architectures bring flexibility to systems implementers. Due to such architectures, there is a need to efficiently compress intermediate feature information for transport over wide area networks (WANs). As feature information differs substantially from conventional image or video data, coding technologies and solutions for machine usage could differ from conventional human-viewing-oriented applications to achieve optimized performance. With the rise of machine learning technologies and machine vision applications, the amount of video and images consumed by machines has rapidly grown. Typical use cases include intelligent transportation, smart city technology, intelligent content management, etc., which incorporate machine vision tasks such as object detection, instance segmentation, and object tracking. Due to the large volume of video data, extracting and compressing the feature from a video is essential for efficient transmission and storage. Feature compression technology solicited in this Call for Evidence (CfE) can also be helpful in other regards, such as computational offloading and privacy protection.
Over the last three years, MPEG has investigated potential technologies for efficiently compressing feature data for machine vision tasks and established an evaluation mechanism that includes feature anchors, rate-distortion-based metrics, and evaluation pipelines. The evaluation framework of VCM depicted below comprises neural network tasks (typically informative) at both ends as well as VCM encoder and VCM decoder, respectively. The normative part of VCM typically includes the bitstream syntax which implicitly defines the decoder whereas other parts are usually left open for industry competition and research.
Further details about the CfP and how interested parties can respond can be found in the official press release here.
Research aspects: the main research area for coding-related standards is certainly compression efficiency (and probably runtime). However, this video coding standard will not target humans as video consumers but as machines. Thus, video quality and, in particular, Quality of Experience needs to be interpreted differently, which could be another worthwhile research dimension to be studied in the future.
Green Metadata
MPEG Systems has been working on Green Metadata for the last ten years to enable the adaptation of the client’s power consumption according to the complexity of the bitstream. Many modern implementations of video decoders can adjust their operating voltage or clock speed to adjust the power consumption level according to the required computational power. Thus, if the decoder implementation knows the variation in the complexity of the incoming bitstream, then the decoder can adjust its power consumption level to the complexity of the bitstream. This will allow less energy use in general and extended video playback for the battery-powered devices.
The third edition enables support for Versatile Video Coding (VVC, ISO/IEC 23090-3, a.k.a. ITU-T H.266) encoded bitstreams and enhances the capability of this standard for real-time communication applications and services. While finalizing the support of VVC, MPEG Systems has also started the development of a new amendment to the Green Metadata standard, adding the support of Essential Video Coding (EVC, ISO/IEC 23094-1) encoded bitstreams.
Research aspects: reducing global greenhouse gas emissions will certainly be a challenge for humanity in the upcoming years. The amount of data on today’s internet is dominated by video, which all consumes energy from production to consumption. Therefore, there is a strong need for explicit research efforts to make video streaming in all facets friendly to our environment.
Third Edition of Common Media Application Format (CMAF)
The third edition of CMAF adds two new media profiles for High Efficiency Video Coding (HEVC, ISO/IEC 23008-2, a.k.a. ITU-T H.265), namely for (i) 8K and (ii) High Frame Rate (HFR). Regarding the former, the media profile supporting 8K resolution video encoded with HEVC (Main 10 profile, Main Tier with 10 bits per colour component) has been added to the list of CMAF media profiles for HEVC. The profile will be branded as ‘c8k0’ and will support videos with up to 7680×4320 pixels (8K) and up to 60 frames per second. Regarding the latter, another media profile has been added to the list of CMAF media profiles, branded as ‘c8k1’ and supports HEVC encoded video with up to 8K resolution and up to 120 frames per second. Finally, chroma location indication support has been added to the 3rd edition of CMAF.
Research aspects: basically, CMAF serves two purposes: (i) harmonizing DASH and HLS at the segment format level by adopting the ISOBMFF and (ii) enabling low latency streaming applications by introducing chunks (that are smaller than segments). The third edition supports resolutions up to 8K and HFR, which raises the question of how low latency can be achieved for 8K/HFR applications and services and under which conditions.
New Amendment for Versatile Supplemental Enhancement Information (VSEI) containing Technology for Neural Network-based Post Filtering
At the 139th MPEG meeting, the MPEG Joint Video Experts Team with ITU-T SG 16 (WG 5; JVET) issued a Committee Draft Amendment (CDAM) text for the Versatile Supplemental Enhancement Information (VSEI) standard (ISO/IEC 23002-7, a.k.a. ITU-T H.274). Beyond the Supplemental Enhancement Information (SEI) message for shutter interval indication, which is already known from its specification in Advanced Video Coding (AVC, ISO/IEC 14496-10, a.k.a. ITU-T H.264) and High Efficiency Video Coding (HEVC, ISO/IEC 23008-2, a.k.a. ITU-T H.265), and a new indicator for subsampling phase indication which is relevant for variable-resolution video streaming, this new amendment contains two SEI messages for describing and activating post filters using neural network technology in video bitstreams. This could reduce coding noise, upsampling, colour improvement, or denoising. The description of the neural network architecture itself is based on MPEG’s neural network coding standard (ISO/IEC 15938-17). Results from an exploration experiment have shown that neural network-based post filters can deliver better performance than conventional filtering methods. Processes for invoking these new post-processing filters have already been tested in a software framework and will be made available in an upcoming version of the Versatile Video Coding (VVC, ISO/IEC 23090-3, a.k.a. ITU-T H.266) reference software (ISO/IEC 23090-16, a.k.a. ITU-T H.266.2).
Research aspects: quality enhancements such as reducing coding noise, upsampling, colour improvement, or denoising have been researched quite substantially either with or without neural networks. Enabling such quality enhancements via (V)SEI messages enable system-level support for research and development efforts in this area. For example, integration in video streaming applications or/and conversational services, including performance evaluations.
The latest MPEG-DASH Update
Finally, I’d like to provide a brief update on MPEG-DASH! At the 139th MPEG meeting, MPEG Systems issued a new working draft related to Extended Dependent Random Access Point (EDRAP) streaming and other extensions, which will be further discussed during the Ad-hoc Group (AhG) period (please join the dash email list for further details/announcements). Furthermore, Defects under Investigation (DuI) and Technologies under Consideration (TuC) have been updated. Finally, a new part has been added (ISO/IEC 23009-9), which is called encoder and packager synchronization, for which also a working draft has been produced. Publicly available documents (if any) can be found here.
An updated overview of DASH standards/features can be found in the Figure below.
Research aspects: in the Christian Doppler Laboratory ATHENA we aim to research and develop novel paradigms, approaches, (prototype) tools and evaluation results for the phases (i) multimedia content provisioning (i.e., video coding), (ii) content delivery (i.e., video networking), and (iii) content consumption (i.e., video player incl. ABR and QoE) in the media delivery chain as well as for (iv) end-to-end aspects, with a focus on, but not being limited to, HTTP Adaptive Streaming (HAS). Recent DASH-related publications include “Low Latency Live Streaming Implementation in DASH and HLS” and “Segment Prefetching at the Edge for Adaptive Video Streaming” among others.
The 140th MPEG meeting will be face-to-face in Mainz, Germany, from October 24-28, 2022. Click here for more information about MPEG meetings and their developments.
