Category Archives: Event Report

Introduction

Welcome to this new column on the ACM SIGMM Records from the Video Quality Experts Group (VQEG), which will provide an overview of the last VQEG plenary meeting that took place from 9 to 13 May 2022. It was organized by INSA Rennes (France), and it was the first face-to-face meeting after the series of online meetings due to the Covid-19 pandemic. Remote attendance was also offered, which made possible that around 100 participants, from 17 different countries, attended the meeting (more than 30 of them attended in person). During the meeting, more than 40 presentations were provided, and interesting discussion took place. All the related information, minutes, and files from the meeting are available online in the VQEG meeting website, and video recordings of the meeting are available in Youtube.

Many of the works presented at this meeting can be relevant for the SIGMM community working on quality assessment. Particularly interesting can be the proposals to update the ITU-T Recommendations P.910 and P.913, as well as the presented publicly available datasets. We encourage those readers interested in any of the activities going on in the working groups to check their websites and subscribe to the corresponding reflectors, to follow them and get involved.

Overview of VQEG Projects

Audiovisual HD (AVHD)

The AVHD group investigates improved subjective and objective methods for analyzing commonly available video systems. In this sense, the group continues working on extensions of the ITU-T Recommendation P.1204 to cover other encoders (e.g., AV1) apart from H.264, HEVC, and VP9. In addition, the project’s Quality of Experience (QoE) Metrics for Live Video Streaming Applications (Live QoE) and Advanced Subjective Methods (AVHD-SUB) are still ongoing. 

In this meeting, several AVHD-related topics were discussed, supported by six different presentations. In the first one, Mikolaj Leszczuk (AGH University, Poland) presented an analysis of the influence on the subjective assessment of the quality of video transmission of experiment conditions, such as video sequence order, variation and repeatability that can entail a “learning” process of the test participants during the test. In the second presentation, Lucjan Janowski (AGH University, Poland) presented two proposals towards more ecologically valid experiment designs: the first one using the Absolute Category Rating [1] without scale but in a “think aloud” manner, and the second one called “Your Youtube, our lab” in which the user selects the content that he or she prefers and a question quality appears during the viewing experience through a specifically designed interface. Also dealing with the study of testing methodologies, Babak Naderi (TU-Berlin, Germany) presented work on subjective evaluation of video quality with a crowdsourcing approach, while Pierre David (Capacités, France) presented a three-lab experiment, involving Capacités (France), RISE (Sweden) and AGH University (Poland) on quality evaluation of social media videos. Kjell Brunnström (RISE, Sweden) continued by giving an overview of video quality assessment of Video Assistant Refereeing (VAR) systems, and lastly, Olof Lindman (SVT, Sweden) presented another effort to reduce the lack of open datasets with the Swedish Television (SVT) Open Content.

Quality Assessment for Health applications (QAH)

The QAH group works on the quality assessment of health applications, considering both subjective evaluation and the development of datasets, objective metrics, and task-based approaches. In this meeting, Lucie Lévêque (Nantes Université, France) provided an overview of the recent activities of the group, including a submitted review paper on objective quality assessment for medical images, a special session accepted for IEEE International Conference on Image Processing (ICIP) that will take place in October in Bordeaux (France), and a paper submitted to IEEE ICIP on quality assessment through detection task of covid-19 pneumonia. The work described in this paper was also presented by Meriem Outtas (INSA Rennes, France).

In addition, there were two more presentations related to the quality assessment of medical images. Firstly, Yuhao Sun (University of Edinburgh, UK) presented their research on a no-reference image quality metric for visual distortions on Computed Tomography (CT) scans [2]. Finally, Marouane Tliba (Université d’Orleans, France) presented his studies on quality assessment of medical images through deep-learning techniques using domain adaptation.

Statistical Analysis Methods (SAM)

The SAM group works on improving analysis methods both for the results of subjective experiments and for objective quality models and metrics. The group is currently working on a proposal to update the ITU-T Recommendation P.913, including new testing methods for subjective quality assessment and statistical analysis of the results. Margaret Pinson presented this work during the meeting.   

In addition, five presentations were delivered addressing topics related to the group activities. Jakub Nawała (AGH University, Poland) presented the Generalised Score Distribution to accurately describe responses from subjective quality experiments. Three presentations were provided by members of Nantes Université (France): Ali Ak presented his work on spammer detection on pairwise comparison experiments, Andreas Pastor talked about how to improve the maximum likelihood difference scaling method in order to measure the inter-content scale, and Chama El Majeny presented the functionalities of a subjective test analysis tool, whose code will be publicly available. Finally, Dietmar Saupe (Univerity of Konstanz, Germany) delivered a presentation on subjective image quality assessment with boosted triplet comparisons.

Computer Generated Imagery (CGI)

CGI group is devoted to analyzing and evaluating computer-generated content, with a focus on gaming in particular. Currently, the group is working on the ITU-T Work Item P.BBQCG on Parametric bitstream-based Quality Assessment of Cloud Gaming Services. Apart from this, Jerry (Xiangxu) Yu (University of Texas at Austin, US) presented a work on subjective and objective quality assessment of user-generated gaming videos and Nasim Jamshidi (TUB, Germany) presented a deep-learning bitstream-based video quality model for CG content.

No Reference Metrics (NORM)

The NORM group is an open collaborative project for developing no-reference metrics for monitoring visual service quality. Currently, the group is working on three topics: the development of no-reference metrics, the clarification of the computation of the Spatial and Temporal Indexes (SI and TI, defined in the ITU-T Recommendation P.910), and on the development of a standard for video quality metadata.  

At this meeting, this was one of the most active groups and the corresponding sessions included several presentations and discussions. Firstly, Yiannis Andreopoulos (iSIZE, UK) presented their work on domain-specific fusion of multiple objective quality metrics. Then, Werner Robitza (AVEQ GmbH/TU Ilmenau, Germany) presented the updates on SI/TI clarification activities, which is leading an update of the ITU-T Recommendation P.910. In addition, Lukas Krasula (Netflix, US) presented their investigations on the relation between banding annoyance and the overall quality perceived by the viewers. Hadi Amirpour (University of Klagenfurt, Austria) delivered two presentations related to their Video Complexity Analyzer and their Video Complexity Dataset, which are both publicly available. Finally, Mikołaj Leszczuk (AGH University , Poland) gave two talks on their research related to User-Generated Content (UGC) (a.k.a. in-the-wild video content) recognition and on advanced video quality indicators to characterise video content.   

Joint Effort Group (JEG) – Hybrid

The JEG group was focused on joint work to develop hybrid perceptual/bitstream metrics and gradually evolved over time to include several areas of Video Quality Assessment (VQA), such as the creation of a large dataset for training such models using full-reference metrics instead of subjective metrics. A report on the ongoing activities of the group was presented by Enrico Masala (Politecnico di Torino, Italy), which included the release of a new website to reflect the evolution that happened in the last few years within the group. Although currently the group is not directly seeking the development of new metrics or tools readily available for VQA, it is still working on related topics such as the studies by Lohic Fotio Tiotsop (Politecnico di Torino, Italy) on the sensitivity of artificial intelligence-based observers to input signal modification.

5G Key Performance Indicators (5GKPI)

The 5GKPI group studies the relationship between key performance indicators of new 5G networks and QoE of video services on top of them. In this meeting, Pablo Pérez (Nokia, Spain) presented an extended report on the group activities, from which it is worth noting the joint work on a contribution to the ITU-T Work Item G.QoE-5G. 

Immersive Media Group (IMG)

The IMG group is focused on the research on the quality assessment of immersive media. Currently, the main joint activity of the group is the development of a test plan for evaluating the QoE of immersive interactive communication systems. In this sense, Pablo Pérez (Nokia, Spain) and Jesús Gutiérrez (Universidad Politécnica de Madrid, Spain) presented a follow up on this test plan including an overview of the state-of-the-art on related works and a taxonomy classifying the existing systems [3]. This test plan is closely related to the work carried out by the ITU-T on QoE Assessment of eXtended Reality Meetings, so Gunilla Berndtsson (Ericsson, Sweden) presented the latest advances on the development of the P.QXM.  

Apart from this, there were four presentations related to the quality assessment of immersive media. Shirin Rafiei (RISE, Sweden) presented a study on QoE assessment of an augmented remote operating system for scaling in smart mining applications. Zhengyu Zhang (INSA Rennes, France) gave a talk on a no-reference quality metric for light field images based on deep-learning and exploiting angular and spatial information. Ali Ak (Nantes Université, France) presented a study on the effect of temporal sub-sampling on the accuracy of the quality assessment of volumetric video. Finally, Waqas Ellahi (Nantes Université, France) showed their research on a machine-learning framework to predict Tone-Mapping Operator (TMO) preference based on image and visual attention features [4].

Quality Assessment for Computer Vision Applications (QACoViA)

The goal of the QACoViA group is to study the visual quality requirements for computer vision methods. In this meeting, there were three presentations related to this topic. Mikołaj Leszczuk (AGH University, Poland) presented an objective video quality assessment method for face recognition tasks. Also, Alban Marie  (INSA Rennes, France) showed an analysis of the correlation of quality metrics with artificial intelligence accuracy. Finally, Lucie Lévêque (Nantes Université, France) gave an overview of a study on the reliability of existing algorithms for facial expression recognition [5]. 

Intersector Rapporteur Group on Audiovisual Quality Assessment (IRG-AVQA)

The IRG-AVQA group studies topics related to video and audiovisual quality assessment (both subjective and objective) among ITU-R Study Group 6 and ITU-T Study Group 12. In this sense, Chulhee Lee (Yonsei University, South Korea) and Alexander Raake (TU Ilmenau, Germany) provided an overview on ongoing activities related to quality assessment within ITU-R and ITU-T.

Other updates

In addition, the Human Factors for Visual Experiences (HFVE), whose objective is to uphold the liaison relation between VQEG and the IEEE standardization group P3333.1, presented their advances in relation to two standards: IEEE P3333.1.3 – Deep-Learning-based assessment of VE based on HF, which has been approved and published, and the IEEE P3333.1.4 on Light field imaging, which has been submitted and is in the process to be approved. Also, although there were not many activities in this meeting within the Implementer’s Guide for Video Quality Metrics (IGVQM) and the Psycho-Physiological Quality Assessment (PsyPhyQA) they are still active. Finally, as a reminder, the VQEG GitHub with tools and subjective labs setup is still online and kept updated.

The next VQEG plenary meeting will take place online in December 2022. Please, see VQEG Meeting information page for more information.

References

[1] ITU, “Subjective video quality assessment methods for multimedia applications”, ITU-T Recommendation P.910, Jul. 2022.
[2] Y. Sun, G. Mogos, “Impact of Visual Distortion on Medical Images”, IAENG International Journal of Computer Science, 1:49, Mar. 2022.
[3] P. Pérez, E. González-sosa, J. Gutiérrez, N. García, “Emerging Immersive Communication Systems: Overview, Taxonomy, and Good Practices for QoE Assessment”, Frontiers in Signal Processing, Jul. 2022.
[4] W. Ellahi, T. Vigier, P. Le Callet, “A machine-learning framework to predict TMO preference based on image and visual attention features”, International Workshop on Multimedia Signal Processing, Oct. 2021.
[5] E. M. Barbosa Sampaio, L. Lévêque, P. Le Callet, M. Perreira Da Silva, “Are facial expression recognition algorithms reliable in the context of interactive media? A new metric to analyse their performance”, ACM International Conference on Interactive Media Experiences, Jun. 2022.

JPEG analyses the responses of the Calls for Proposals for the standardisation of the first codecs based on machine learning

The 96^th JPEG meeting was held online from 25 to 29 July 2022. The meeting was one of the most productive in the recent history of JPEG with the analysis of the responses of two Calls for Proposals (CfP) for machine learning-based coding solutions, notably JPEG AI and JPEG Pleno Point Cloud Coding. The superior performance of the CfP responses compared to the state-of-the-art anchors leave little doubt about the future of coding technologies becoming dominated by machine learning-based solutions with the expected consequences on the standardisation pathway. A new era of multimedia coding standardisation has begun. Both activities had defined a verification model, and are pursuing a collaborative process that will select the best technologies for the definition of the new machine learning-based standards.

The 96^th JPEG meeting had the following highlights:

• JPEG AI response to the Call for Proposals;
• JPEG Pleno Point Cloud begins the collaborative standardisation phase;
• JPEG Fake Media and NFT
• JPEG Systems
• JPEG Pleno Light Field
• JPEG AIC
• JPEG XS
• JPEG 2000
• JPEG DNA

The following summarises the major achievements of the 96^th JPEG meeting.

JPEG AI

The 96th JPEG meeting represents an important milestone for the JPEG AI standardisation as it marks the beginning of the collaborative phase of this project. The main JPEG AI objective is to design a solution that offers significant compression efficiency improvement over coding standards in common use at equivalent subjective quality and an effective compressed domain processing for machine learning-based image processing and computer vision tasks. 

During the 96th JPEG meeting, several activities occurred, notably presentation of the eleven responses to all tracks of the Call for Proposals (CfP). Furthermore, discussions on the evaluation process used to assess submissions to the CfP took place, namely, subjective, objective and complexity assessment as well as the identification of device interoperability issues by cross-checking. For the standard reconstruction track, several contributions showed significantly higher compression efficiency in both subjective quality methodologies and objective metrics when compared to the best-performing conventional image coding.

From the analysis and discussion of the results obtained, the most promising technologies were identified and a new JPEG AI verification model under consideration (VMuC) was approved. The VMuC corresponds to a combination of two proponents’ solutions (following the ‘one tool for one functionality’ principle), selected by consensus and considering the CfP decision criteria and factors. In addition, a set of JPEG AI Core Experiments were defined to obtain further improvements in both performance efficiency and complexity, notably the use of learning-based GAN training, alternative analysis/synthesis transforms and an evaluation study for the compressed-domain denoising as an image processing task. Several further activities were also discussed and defined, such as the design of a compressed domain image classification decoder VMuC, the creation of a large screen content dataset for the training of learning-based image coding solutions and the definition of a new and larger JPEG AI test set.

JPEG Pleno Point Cloud begins collaborative standardisation phase

JPEG Pleno integrates various modalities of plenoptic content under a single framework in a seamless manner. Efficient and powerful point cloud representation is a key feature of this vision. A point cloud refers to data representing positions of points in space, expressed in a given three-dimensional coordinate system, the so-called geometry. This geometrical data can be accompanied by per-point attributes of varying nature (e.g. color or reflectance). Such datasets are usually acquired with a 3D scanner, LIDAR or created using 3D design software and can subsequently be used to represent and render 3D surfaces. Combined with other types of data (like light field data), point clouds open a wide range of new opportunities, notably for immersive browsing and virtual reality applications.

Learning-based solutions are the state of the art for several computer vision tasks, such as those requiring a high-level understanding of image semantics, e.g., image classification, face recognition and object segmentation, but also 3D processing tasks, e.g. visual enhancement and super-resolution. Recently, learning-based point cloud coding solutions have shown great promise to achieve competitive compression efficiency compared to available conventional point cloud coding solutions at equivalent subjective quality. Building on a history of successful and widely adopted coding standards, JPEG is well positioned to develop a standard for learning-based point cloud coding.

During its 94th meeting, the JPEG Committee released a Final Call for Proposals on JPEG Pleno Point Cloud Coding. This call addressed learning-based coding technologies for point cloud content and associated attributes with emphasis on both human visualization and decompressed/reconstructed domain 3D processing and computer vision with competitive compression efficiency compared to point cloud coding standards in common use, with the goal of supporting a royalty-free baseline. During its 96th meeting, the JPEG Committee evaluated 5 codecs submitted in response to this Call. Following a comprehensive evaluation process, the JPEG Committee selected one of the proposals to form the basis of a future standard and initialised a sub-division to form Part 6 of ISO/IEC 21794. The selected submission was a learning-based approach to point cloud coding that met the requirements of the Call and showed competitive performance, both in terms of coding geometry and color, against existing solutions.

JPEG Fake Media and NFT

At the 96th JPEG meeting, 6 pre-registrations to the Final Call for Proposals (CfP) on JPEG Fake Media were received. The scope of JPEG Fake Media is the creation of a standard that can facilitate the secure and reliable annotation of media asset creation and modifications. The standard shall address use cases that are in good faith as well as those with malicious intent. The CfP welcomes contributions that address at least one of the extensive list of requirements specified in the associated “Use Cases and Requirements for JPEG Fake Media” document. Proponents who have not yet made a pre-registration are still welcome to submit their final proposal before 19 October 2022. Full details about the timeline, submission requirements and evaluation processes are documented in the CfP available on jpeg.org.

In parallel with the work on Fake Media, JPEG explores use cases and requirements related to Non Fungible Tokens (NFTs). Although the use cases between both topics are different, there is a significant overlap in terms of requirements and relevant solutions. The presentations and video recordings of the joint 5th JPEG NFT and Fake Media Workshop that took place prior to the 96^th meeting are available on the JPEG website. In addition, a new version of the “Use Cases and Requirements for JPEG NFT” was produced and made publicly available for review and feedback.

JPEG Systems

During the 96th JPEG Meeting, the IS texts for both JLINK (ISO/IEC 19566-7) and JPEG Snack (ISO/IEC 19566-8) were prepared and submitted for final publication. JLINK specifies a format to store multiple images inside of JPEG files and supports interactive navigation between them. JLINK addresses use cases such as virtual museum tours, real estate visits, hotspot zoom into other images and many others. JPEG Snack on the other hand enables self-running multimedia experiences such as animated image sequences and moving image overlays. Both standards are based on the JPEG Universal Metadata Box Format (JUMBF, ISO/IEC 19566-5) for which a second edition is in progress. This second edition adds extensions to the native support of CBOR (Concise Binary Object Representation) and attaches private fields to the JUMBF Description Box.

JPEG Pleno Light Field

During its 96th meeting, the JPEG Committee released the “JPEG Pleno Second Draft Call for Contributions on Light Field Subjective Quality Assessment”, to collect new procedures and best practices for light field subjective quality evaluation methodologies to assess artefacts induced by coding algorithms. All contributions, which can be test procedures, datasets, and any additional information, will be considered to develop the standard by consensus among JPEG experts following a collaborative process approach. The Final Call for Contributions will be issued at the 97th JPEG meeting. The deadline for submission of contributions is 1 April 2023.

A JPEG Pleno Light Field AhG has also started the preparation of a first workshop on Subjective Light Field Quality Assessment and a second workshop on Learning-based Light field Coding, to exchange experiences, to present technological advances and research results on light field subjective quality assessment and to present technological advances and research results on learning-based coding solutions for light field data, respectively.

JPEG AIC

During its 96th meeting, a Second Draft Call for Contributions on Subjective Image Quality Assessment was issued. The final Call for Contributions is now planned to be issued at the 97^th JPEG meeting. The standardization process will be collaborative from the very beginning, i.e. all submissions will be considered in developing the next extension of the JPEG AIC standard. The deadline for submissions has been extended to 1 April 2023 at 23:59 UTC. Multiple types of contributions are accepted, namely subjective assessment methods including supporting evidence and detailed description, test material, interchange format, software implementation, criteria and protocols for evaluation, additional relevant use cases and requirements, and any relevant evidence or literature. A dataset of sample images with compression-based distortions in the target quality range is planned to be prepared for the 97^th JPEG meeting.

JPEG XS

With the 2nd edition of JPEG XS now in place, the JPEG Committee continues with the development of the 3rd edition of JPEG XS Part 1 (Core coding system) and Part 2 (Profiles and buffer models). These editions will address new use cases and requirements for JPEG XS by defining additional coding tools to further improve the coding efficiency, while keeping the low-latency and low-complexity core aspects of JPEG XS. The primary goal of the 3rd edition is to deliver the same image quality as the 2nd edition, but for specific content such as screen content with half of the required bandwidth. In this respect, experiments have indicated that it is possible to increase the quality in static regions of an image sequence by more than 10dB when compared to the 2nd edition. Based on the input contributions, a first working draft for 21122-1 has been created, along with the necessary core experiments for further evaluation and verification.

In addition, JPEG has finalized the work on the amendment for Part 2 2nd edition that defines a new High 4:2:0 profile and the new sublevel Sublev4bpp. This amendment is now ready for publication by ISO. In the context of Part 4 (Conformance testing) and Part 5 (Reference software), the JPEG Committee decided to make both parts publicly available.

Finally, the JPEG Committee decided to create a series of public documents, called the “JPEG XS in-depth series” that will explain various features and applications of JPEG XS to a broad audience. The first document in this series explains the advantages of using JPEG XS for raw image compression and will be published soon on jpeg.org.

JPEG 2000

The JPEG Committee published a case study that compares HT2K, ProRes and JPEG 2000 Part 1 when processing motion picture content with widely available commercial software tools running on notebook computers, available at https://ds.jpeg.org/documents/jpeg2000/wg1n100269-096-COM-JPEG_Case_Study_HTJ2K_performance_on_laptop_desktop_PCs.pdf

JPEG 2000 is widely used in the media and entertainment industry for Digital Cinema distribution, studio video masters and broadcast contribution links. High Throughput JPEG 2000 (HTJ2K or JPEG 2000 Part 15) is an update to JPEG 2000 that provides an order of magnitude speed up over legacy JPEG 2000 Part 1.

JPEG DNA

The JPEG Committee has continued its exploration of the coding of images in quaternary representations, as it is particularly suitable for DNA storage applications. The scope of JPEG DNA is the creation of a standard for efficient coding of images that considers biochemical constraints and offers robustness to noise introduced by the different stages of the storage process that is based on DNA synthetic polymers. During the 96th JPEG meeting, a new version of the overview document on Use Cases and Requirements for DNA-based Media Storage was issued and has been made publicly available. The JPEG Committee also updated two additional documents: the JPEG DNA Benchmark Codec and the JPEG DNA Common Test Conditions in order to allow for concrete exploration experiments to take place. This will allow further validation and extension of the JPEG DNA benchmark codec to simulate an end-to-end image storage pipeline using DNA and in particular, include biochemical noise simulation which is an essential element in practical implementations. A new branch has been created in the JPEG Gitlab that now contains two anchors and two JPEG DNA benchmark codecs.

Final Quote

“After successful calls for contributions, the JPEG Committee sets precedence by launching the collaborative phase of two learning based visual information coding standards, hence announcing the start of a new era in coding technologies relying on AI.” said Prof. Touradj Ebrahimi, the Convenor of the JPEG Committee.

Upcoming JPEG meetings are planned as follows:

• No 97, will be held online from 24-28 October 2022.
• No 98, will be in Sydney, Australia from 14-20 January 2022

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.

JPEG issues a call for proposals for JPEG Fake Media

The 95^th JPEG meeting was held online from 25 to 29 April 2022. A Call for Proposals (CfP) was issued for JPEG Fake Media that aims at a standardisation framework for secure annotation of modifications in media assets. With this new initiative, JPEG endeavours to provide standardised means for the identification of the provenance of media assets that include imaging information. Assuring the provenance of the coded information is essential considering the current trends and possibilities on multimedia technology.

This new initiative complements the ongoing standardisation of machine learning based codecs for images and point clouds. Both are expected to revolutionise the state of the art of coding standards, leading to compression rates beyond the current state of the art.

The 95^th JPEG meeting had the following highlights:

• JPEG Fake Media issues a Call for Proposals;
• JPEG AI
• JPEG Pleno Point Cloud Coding;
• JPEG Pleno Light Fields quality assessment;
• JPEG AIC near perceptual lossless quality assessment;
• JPEG NFT exploration;
• JPEG DNA explorations
• JPEG XS 2nd edition published;
• JPEG XL 2nd edition.

The following summarises the major achievements of the 95^th JPEG meeting.

JPEG Fake Media

At its 95th JPEG meeting, the committee issued a Final Call for Proposals (CfP) on JPEG Fake Media. The scope of JPEG Fake Media is the creation of a standard that can facilitate the secure and reliable annotation of media asset creation and modifications. The standard shall address use cases that are in good faith as well as those with malicious intent. The call for proposals welcomes contributions that address at least one of the extensive list of requirements specified in the associated “Use Cases and Requirements for JPEG Fake Media” document. Proponents are highly encouraged to express their interest in submission of a proposal before 20 July 2022 and submit their final proposal before 19 October 2022. Full details about the timeline, submission requirements and evaluation processes are documented in the CfP available on jpeg.org.

JPEG AI

Following the JPEG AI joint ISO/IEC/ITU-T Call for Proposals issued after the 94th JPEG committee meeting, 14 registrations were received among which 12 codecs were submitted for the standard reconstruction task. For computer vision and image processing tasks, several teams have submitted compressed domain decoders, notably 6 for image classification. Prior to the 95th JPEG meeting, the work was focused on the management of the Call for Proposals submissions and the creation of the test sets and the generation of anchors for standard reconstruction, image processing and computer vision tasks. Moreover, a dry run of the subjective evaluation of the JPEG AI anchors was performed with expert subjects and the results were analysed during this meeting, followed by additions and corrections to the JPEG AI Common Training and Test Conditions and the definition of several recommendations for the evaluation of the proposals, notably, the anchors, images and bitrates selection. A procedure for cross-check evaluation was also discussed and approved. The work will now focus on the evaluation of the Call for Proposals submissions, which is expected to be finalized at the 96th JPEG meeting.

JPEG Pleno Point Cloud Coding

JPEG Pleno is working towards the integration of various modalities of plenoptic content under a single and seamless framework. Efficient and powerful point cloud representation is a key feature within this vision. Point cloud data supports a wide range of applications for human and machine consumption including metaverse, autonomous driving, computer-aided manufacturing, entertainment, cultural heritage preservation, scientific research and advanced sensing and analysis. During the 95th JPEG meeting, the JPEG Committee reviewed the responses to the Final Call for Proposals on JPEG Pleno Point Cloud Coding. Four responses have been received from three different institutions. At the upcoming 96th JPEG meeting, the responses to the Call for Proposals will be evaluated with a subjective quality evaluation and objective metric calculations.

JPEG Pleno Light Field

The JPEG Pleno standard tools provide a framework for coding new imaging modalities derived from representations inspired by the plenoptic function. The image modalities addressed by the current standardization activities are light field, holography, and point clouds, where these image modalities describe different sampled representations of the plenoptic function. Therefore, to properly assess the quality of these plenoptic modalities, specific subjective and objective quality assessment methods need to be designed.

In this context, JPEG has launched a new standardisation effort known as JPEG Pleno Quality Assessment. It aims at providing a quality assessment standard, defining a framework that includes subjective quality assessment protocols and objective quality assessment procedures for lossy decoded data of plenoptic modalities for multiple use cases and requirements. The first phase of this effort will address the light field modality.

To assist this task, JPEG has issued the “JPEG Pleno Draft Call for Contributions on Light Field Subjective Quality Assessment”, to collect new procedures and best practices with regard to light field subjective quality assessment methodologies to assess artefacts induced by coding algorithms. All contributions, which can be test procedures, datasets, and any additional information, will be considered to develop the standard by consensus among the JPEG experts following a collaborative process approach.

The Final Call for Contributions will be issued at the 96th JPEG meeting. The deadline for submission of contributions is 18 December 2022.

JPEG AIC

During the 95th JPEG Meeting, the committee released the Draft Call for Contributions on Subjective Image Quality Assessment.

The new JPEG AIC standard will be developed considering all the submissions to the Call for Contributions in a collaborative process. The deadline for the submission is set for 14 October 2022. Multiple types of contributions are accepted, notably subjective assessment methods including supporting evidence and detailed description, test material, interchange format, software implementation, criteria and protocols for evaluation, additional relevant use cases and requirements, and any relevant evidence or literature.

The JPEG AIC committee has also started the preparation of a workshop on subjective assessment methods for the investigated quality range, which will be held at the end of June. The workshop targets obtaining different views on the problem, and will include both internal and external speakers, as well as a Q&A panel. Experts in the field of quality assessment and stakeholders interested in the use cases are invited.

JPEG NFT

After the joint JPEG NFT and Fake Media workshops it became evident that even though the use cases between both topics are different, there is a significant overlap in terms of requirements and relevant solutions. For that reason, it was decided to create a single AHG that covers both JPEG NFT and JPEG Fake Media explorations. The newly established AHG JPEG Fake Media and NFT will use the JPEG Fake Media mailing list.

JPEG DNA

The JPEG Committee has continued its exploration of the coding of images in quaternary representations, as it is particularly suitable for DNA storage applications. The scope of JPEG DNA is the creation of a standard for efficient coding of images that considers biochemical constraints and offers robustness to noise introduced by the different stages of the storage process that is based on DNA synthetic polymers. A new version of the overview document on DNA-based Media Storage: State-of-the-Art, Challenges, Use Cases and Requirements was issued and has been made publicly available. It was decided to continue this exploration by validating and extending the JPEG DNA benchmark codec to simulate an end-to-end image storage pipeline using DNA for future exploration experiments including biochemical noise simulation. During the 95th JPEG meeting, a new specific document describing the Use Cases and Requirements for DNA-based Media Storage was created which is made publicly available. A timeline for the standardization process was also defined. Interested parties are invited to consider joining the effort by registering to the JPEG DNA AHG mailing list.

JPEG XS

The JPEG Committee is pleased to announce that the 2nd editions of Part 1 (Core coding system), Part 2 (Profiles and buffer models), and Part 3 (Transport and container formats) were published in March 2022. Furthermore, the committee finalized the work on Part 4 (Conformance testing) and Part 5 (Reference software), which are now entering the final phase for publication. With these last two parts, the committee’s work on the 2nd edition of the JPEG XS standards comes to an end, allowing to shift the focus to further improve the standard. Meanwhile, in response to the latest Use Cases and Requirements for JPEG XS v3.1, the committee received a number of technology proposals from Fraunhofer and intoPIX that focus on improving the compression performance for desktop content sequences. The proposals will now be evaluated and thoroughly tested and will form the foundation of the work towards a 3rd edition of the JPEG XS suite of standards. The primary goal of the 3rd edition is to deliver the same image quality as the 2nd edition, but with half of the required bandwidth.

JPEG XL

The second edition of JPEG XL Part 1 (Core coding system), with an improved numerical stability of the edge-preserving filter and numerous editorial improvements, has proceeded to the CD stage. Work on a second edition of Part 2 (File format) was initiated. Hardware coding was also further investigated. Preliminary software support has been implemented in major web browsers, image viewing and editing software, including popular tools such as FFmpeg, ImageMagick, libvips, GIMP, GDK and Qt. JPEG XL is now ready for wide-scale adoption.

Final Quote

“Recent development on creation and modification of visual information call for development of tools that can help protecting the authenticity and integrity of media assets. JPEG Fake Media is a standardised framework to deal with imaging provenance.” said Prof. Touradj Ebrahimi, the Convenor of the JPEG Committee.

Upcoming JPEG meetings are planned as follows:

• No. 96, will be held online during 25-29 July 2022.

Introduction

Welcome to a new column on the ACM SIGMM Records from the Video Quality Experts Group (VQEG).
The last VQEG plenary meeting took place from 13 to 17 December 2021, and it was organized online by University of Surrey, UK. During five days, more than 100 participants (from more than 20 different countries of America, Asia, Africa, and Europe) could remotely attend the multiple sessions related to the active VQEG projects, which included more than 35 presentations and interesting discussions. This column provides an overview of this VQEG plenary meeting, while all the information, minutes and files (including the presented slides) from the meeting are available online in the VQEG meeting website.

Many of the works presented in this meeting can be relevant for the SIGMM community working on quality assessment. Particularly interesting can be the new analyses and methodologies discussed within the Statistical Analyses Methods group, the new metrics and datasets presented within the No-Reference Metrics group, and the progress on the plans of the 5G Key Performance Indicators group and the Immersive Media group. We encourage those readers interested in any of the activities going on in the working groups to check their websites and subscribe to the corresponding reflectors, to follow them and get involved.

Overview of VQEG Projects

Audiovisual HD (AVHD)

The AVHD group investigates improved subjective and objective methods for analyzing commonly available video systems. In this sense, it has recently completed a joint project between VQEG and ITU SG12 in which 35 candidate objective quality models were submitted and evaluated through extensive validation tests. The result was the ITU-T Recommendation P.1204, which includes three standardized models: a bit-stream model, a reduced reference model, and a hybrid no-reference model. The group is currently considering extensions of this standard, which originally covered H.264, HEVC, and VP9, to include other encoders, such as AV1. Apart from this, two other projects are active under the scope of AVHD: QoE Metrics for Live Video Streaming Applications (Live QoE) and Advanced Subjective Methods (AVHD-SUB).

During the meeting, three presentations related to AVHD activities were provided. In the first one, Mikolaj Leszczuk (AGH University) presented their work on secure and reliable delivery of professional live transmissions with low latency, which brought to the floor the constant need for video datasets, such as the VideoSet. In addition, Andy Quested (ITU-R Working Party 6C) led a discussion on how to assess video quality for very high resolution (e.g., 8K, 16K, 32K, etc.) monitors with interactive applications, which raised the discussion on the key possibility of zooming in to absorb the details of the images without pixelation. Finally, Abhinau Kumar (UT Austin) and Cosmin Stejerean (Meta) presented their work on exploring the reduction of the complexity of VMAF by using features in the wavelet domain [1]. 

Quality Assessment for Health applications (QAH)

The QAH group works on the quality assessment of health applications, considering both subjective evaluation and the development of datasets, objective metrics, and task-based approaches. This group was recently launched and, for the moment, they have been working on a topical review paper on objective quality assessment of medical images and videos, which was submitted in December to Medical Image Analysis [2]. Rafael Rodrigues (Universidade da Beira Interior) and Lucie Lévêque (Nantes Université) presented the main details of this work in a presentation scheduled during the QAH session. The presentation also included information about the review paper published by some members of the group on methodologies for subjective quality assessment of medical images [3] and the efforts in gathering datasets to be listed on the VQEG datasets website. In addition, Lu Zhang (IETR – INSA Rennes) presented her work on model observers for the objective quality assessment of medical images from task-based approaches, considering three tasks: detection, localization, and characterization [4]. In addition, it is worth noting that members of this group are organizing a special session on “Quality Assessment for Medical Imaging” at the IEEE International Conference on Image Processing (ICIP) that will take place in Bordeaux (France) from the 16 to the 19 October 2022.

Statistical Analysis Methods (SAM)

The SAM group works on improving analysis methods both for the results of subjective experiments and for objective quality models and metrics. Currently, they are working on statistical analysis methods for subjective tests, which are discussed in their monthly meetings.

In this meeting, there were four presentations related to SAM activities. In the first one, Zhi Li and Lukáš Krasula (Netflix), exposed the lessons they learned from the subjective assessment test carried out during the development of their metric Contrast Aware Multiscale Banding Index (CAMBI) [5]. In particular, they found that some subjective can have perceptually unbalanced stimuli, which can cause systematic and random errors in the results. In this sense, they explained their statistical data analyses to mitigate these errors, such as the techniques in ITU-T Recommendation P.913 (section 12.6) which can reduce the effects of the random error. The second presentation described the work by Pablo Pérez (Nokia Bell Labs), Lucjan Janowsk (AGH University), Narciso Garcia (Universidad Politécnica de Madrid), and Margaret H. Pinson (NTIA/ITS) on a novel subjective assessment methodology with few observers with repetitions (FOWR) [6]. Apart from the description of the methodology, the dataset generated from the experiments is available on the Consumer Digital Video Library (CDVL). Also, they launched a call for other labs to repeat their experiments, which will help on discovering the viability, scope and limitations of the FOWR method and, if appropriate, include this method in the ITU-T Recommendation P.913 for quasi-experimental assessments when it is not possible to have 16 to 24 subjects (e.g., pre-tests, expert assessment, and resource limitations), for example, performing the experiment with 4 subjects 4 times each on different days, which would be similar to a test with 15 subjects. In the third presentation, Irene Viola (CWI) and Lucjan Janowski (AGH University) presented their analyses on the standardized methods for subject removal in subjective tests. In particular, the methods proposed in the recommendations ITU-R BT.500 and ITU-T P.913 were considered, resulting in that the first one (described in Annex 1 of Part 1) is not recommended for Absolute Category Rating (ACR) tests, while the one described in the second recommendations provides good performance, although further investigation in the correlation threshold used to discard subjects s required. Finally, the last presentation led the discussion on the future activities of SAM group, where different possibilities were proposed, such as the analysis of confidence intervals for subjective tests, new methods for comparing subjective tests from more than two labs, how to extend these results to better understand the precision of objective metrics, and research on crowdsourcing experiment in order to make them more reliable and improve cost-effectiveness. These new activities are discussed in the monthly meetings of the group.

Computer Generated Imagery (CGI)

CGI group focuses on quality analysis of computer-generated imagery, with a focus on gaming in particular. Currently, the group is working on topics related to ITU work items, such as ITU-T Recommendation P.809 with the development of a questionnaire for interactive cloud gaming quality assessment, ITU-T Recommendation P.CROWDG related to quality assessment of gaming through crowdsourcing, ITU-T Recommendation P.BBQCG with a bit-stream based quality assessment of cloud gaming services, and a codec comparison for computer-generated content. In addition, a presentation was delivered during the meeting by Nabajeet Barman (Kingston University/Brightcove), who presented the subjective results related to the work presented at the last VQEG meeting on the use of LCEVC for Gaming Video Streaming Applications [7]. For more information on the related activities, do not hesitate to contact the chairs of the group. 

No Reference Metrics (NORM)

The NORM group is an open collaborative project for developing no-reference metrics for monitoring visual service quality. Currently, two main topics are being addressed by the group, which are discussed in regular online meetings. The first one is related to the improvement of SI/TI metrics to solve ambiguities that have appeared over time, with the objective of providing reference software and updating the ITU-T Recommendation P.910. The second item is related to the addition of standard metadata of video quality assessment-related information in the encoded video streams. 

In this meeting, this group was one of the most active in terms of presentations on related topics, with 11 presentations. Firstly, Lukáš Krasula (Netflix) presented their Contrast Aware Multiscale Banding Index (CAMBI) [5], an objective quality metric that addresses banding degradations that are not detected by other metrics, such as VMAF and PSNR (code is available on GitHub). Mikolaj Leszczuk (AGH University) presented their work on the detection of User-Generated Content (UGC) automatic detection in the wild. Also, Vignesh Menon & Hadi Amirpour (AAU Klagenfurt) presented their open-source project related to the analysis and online prediction of video complexity for streaming applications. Jing Li (Alibaba) presented their work related to the perceptual quality assessment of internet videos [8], proposing a new objective metric (STDAM, for the moment, used internally) validated in the Youku-V1K dataset. The next presentation was delivered by Margaret Pinson (NTIA/ITS) dealing with a comprehensive analysis on why no-reference metrics fail, which emphasized the need of training these metrics on several datasets and test them on larger ones. The discussion also pointed out the recommendation for researchers to publish their metrics in open source in order to make it easier to validate and improve them. Moreover, Balu Adsumilli and Yilin Wang (Youtube) presented a new no-reference metric for UGC, called YouVQ, based on a transfer-learning approach with a pre-train on non-UGC data and a re-train on UGC. This metric will be released in open-source shortly, and a dataset with videos and subjective scores has been also published. Also, Margaret Pinson (NTIA/ITS), Mikołaj Leszczuk (AGH University), Lukáš Krasula (Netflix), Nabajeet Barman (Kingston University/Brightcove), Maria Martini (Kingston University), and Jing Li (Alibaba) presented a collection of datasets for no-reference metric research, while Shahid Satti (Opticom GmbH) exposed their work on encoding complexity for short video sequences. On his side, Franz Götz-Hahn (Universität Konstanz/Universität Kassel) presented their work on the creation of the KonVid-150k video quality assessment dataset [9], which can be very valuable for training no-reference metrics, and the development of objective video quality metrics. Finally, regarding the aforementioned two active topics within NORM group, Ioannis Katsavounidis (Meta) provided a presentation on the advances in relation to the activity related to the inclusion of standard video quality metadata, while Lukáš Krasula (Netflix), Cosmin Stejerean (Meta), and Werner Robitza (AVEQ/TU Ilmenau) presented the updates on the improvement of SI/TI metrics for modern video systems.

Joint Effort Group (JEG) – Hybrid

The JEG group was focused on joint work to develop hybrid perceptual/bitstream metrics and on the creation of a large dataset for training such models using full-reference metrics instead of subjective metrics. In this sense, a project in collaboration with Sky was finished and presented in the last VQEG meeting.

Related activities were presented in this meeting. In particular, Enrico Masala and Lohic Fotio Tiotsop (Politecnico di Torino) presented the updates on the recent activities carried out by the group, and their work on artificial-intelligence observers for video quality evaluation [10].

Implementer’s Guide for Video Quality Metrics (IGVQM)

The IGVQM group, whose activity started in the VQEG meeting in December 2020, works on creating an implementer’s guide for video quality metrics. In this sense, the current goal is to create a report on the accuracy of video quality metrics following a test plan based on collecting datasets, collecting metrics and methods for assessment, and carrying out statistical analyses. An update on the advances was provided by Ioannis Katsavounidis (Meta) and a call for the community is open to contribute to this activity with datasets and metrics.

5G Key Performance Indicators (5GKPI)

The 5GKPI group studies relationship between key performance indicators of new communications networks (especially 5G) and QoE of video services on top of them. Currently, the group is working on the definition of relevant use cases, which are discussed on monthly audiocalls. 

In relation to these activities, there were four presentations during this meeting. Werner Robitza (AVQ/TU Ilmenau) presented a proposal for KPI message format for gaming QoE over 5G networks. Also, Pablo Pérez (Nokia Bell Labs) presented their work on a parametric quality model for teleoperated driving [11] and an update of the ITU-T GSTR-5GQoE topic, related to the QoE requirements for real-time multimedia services over 5G networks. Finally, Margaret Pinson (NTIA/ITS) presented an overall description of 5G technology, including differences in spectrum allocation per country impact on the propagation and responsiveness and throughput of 5G devices.

Immersive Media Group (IMG)

The IMG group researches on quality assessment of immersive media. The group recently finished the test plan for quality assessment of short 360-degree video sequences, which resulted in the support for the development of the ITU-T Recommendation P.919. Currently, the group is working on further analyses of the data gathered from the subjective tests carried out for that test plan and on the analysis of data for the quality assessment of long 360-degree videos. In addition, members of the group are contributing to the IUT-T SG12 on the topic G.CMVTQS on computational models for QoE/QoS monitoring to assess video telephony services. Finally, the group is also working on the preparation of a test plan for evaluating the QoE with immersive and interactive communication systems, which was presented by Pablo Pérez (Nokia Bell Labs) and Jesús Gutiérrez (Universidad Politécnica de Madrid). If the reader is interested in this topic, do not hesitate to contact them to join the effort. 

During the meeting, there were also four presentations covering topics related to the IMG topics. Firstly, Alexander Raake (TU Ilmenau) provided an overview of the projects within the AVT group dealing with the QoE assessment of immersive media. Also, Ashutosh Singla (TU Ilmenau) presented a 360-degree video database with higher-order ambisonics spatial audio. Maria Martini (Kingston University) presented an update on the IEEE standardization activities on Human Factors or Visual Experiences (HFVE), such as the recently submitted draft standard on deep-learning-based quality assessment and the draft standard to be submitted shortly on quality assessment of light field content. Finally, Kjell Brunnstöm (RISE) presented their work on legibility in virtual reality, also addressing the perception of speech-to-text by Deaf and hard of hearing.  

Intersector Rapporteur Group on Audiovisual Quality Assessment (IRG-AVQA) and Q19 Interim Meeting

Although in this case there was no official meeting IRG-AVQA meeting, there were various presentations related to ITU activities addressing QoE evaluation topics. In this sense, Chulhee Lee (Yonsei University) presented an overview of ITU-R activities, with a special focus on quality assessment of HDR content, and together with Alexander Raake (TU Ilmenau) presented an update on ongoing ITU-T activities.

Other updates

All the sessions of this meeting and, thus, the presentations, were recorded and have been uploaded to Youtube. Also, it is worth informing that the anonymous FTP will be closed soon, so files and presentations can be accessed from old browsers or via an FTP app. All the files, including those corresponding to the VQEG meetings, will be embedded into the VQEG website over the next months. In addition, the GitHub with tools and subjective labs setup is still online and kept updated. Moreover, during this meeting, it was decided to close the Joint Effort Group (JEG) and the Independent Lab Group (ILG), which can be re-established when needed. Finally, although there were not many activities in this meeting within the Quality Assessment for Computer Vision Applications (QACoViA) and the Psycho-Physiological Quality Assessment (PsyPhyQA) they are still active.

The next VQEG plenary meeting will take place in Rennes (France) from 9 to 13 May 2022, which will be again face-to-face after four online meetings.

References

[1] A. K. Venkataramanan, C. Stejerean, A. C. Bovik, “FUNQUE: Fusion of Unified Quality Evaluators”, arXiv:2202.11241, submitted to the IEEE International Conference on Image Processing (ICIP), 2022. (opens in a new tab).
[2] R. Rodrigues, L. Lévêque, J. Gutiérrez, H. Jebbari, M. Outtas, L. Zhang, A. Chetouani, S. Al-Juboori, M. G. Martini, A. M. G. Pinheiro, “Objective Quality Assessment of Medical Images and Videos: Review and Challenges”, submitted to the Medical Image Analysis, 2022.
[3] L. Lévêque, M. Outtas, L. Zhang, H. Liu, “Comparative study of the methodologies used for subjective medical image quality assessment”, Physics in Medicine & Biology, vol. 66, no. 15, Jul. 2021. (opens in a new tab).
[4] L.Zhang, C.Cavaro-Ménard, P.Le Callet, “An overview of model observers”, Innovation and Research in Biomedical Engineering, vol. 35, no. 4, pp. 214-224, Sep. 2014. (opens in a new tab).
[5] P. Tandon, M. Afonso, J. Sole, L. Krasula, “Comparative study of the methodologies used for subjective medical image quality assessment”, Picture Coding Symposium (PCS), Jul. 2021. (opens in a new tab).
[6] P. Pérez, L. Janowski, N. García, M. Pinson, “Subjective Assessment Experiments That Recruit Few Observers With Repetitions (FOWR)”, IEEE Transactions on Multimedia (Early Access), Jul. 2021. (opens in a new tab).
[7] N. Barman, S. Schmidt, S. Zadtootaghaj, M.G. Martini, “Evaluation of MPEG-5 part 2 (LCEVC) for live gaming video streaming applications”, Proceedings of the Mile-High Video Conference, Mar. 2022. (opens in a new tab).
[8] J. Xu, J. Li, X. Zhou, W. Zhou, B. Wang, Z. Chen, “Perceptual Quality Assessment of Internet Videos”, Proceedings of the ACM International Conference on Multimedia, Oct. 2021. (opens in a new tab).
[9] F. Götz-Hahn, V. Hosu, H. Lin, D. Saupe, “KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild”, IEEE Access, vol. 9, pp. 72139 – 72160, May. 2021. (opens in a new tab).
[10] L. F. Tiotsop, T. Mizdos, M. Barkowsky, P. Pocta, A. Servetti, E. Masala, “Mimicking Individual Media Quality Perception with Neural Network based Artificial Observers”, ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 18, no. 1, Jan. 2022. (opens in a new tab).
[11] P. Pérez, J. Ruiz, I. Benito, R. López, “A parametric quality model to evaluate the performance of tele-operated driving services over 5G networks”, Multimedia Tools and Applications, Jul. 2021. (opens in a new tab).