Category Archives: 0121

Introduction

Welcome to the fourth column on the ACM SIGMM Records from the Video Quality Experts Group (VQEG).
During the last VQEG plenary meeting (14-18 Dec. 2020) various interesting discussions arose regarding new topics not addressed up to then by VQEG groups, which led to launching three new sub-projects and a new project related to: 1) clarifying the computation of spatial and temporal information (SI and TI), 2) including video quality metrics as metadata in compressed bitstreams, 3) Quality of Experience (QoE) metrics for live video streaming applications, and 4) providing guidelines on implementing objective video quality metrics to the video compression community.
The following sections provide more details about these new activities and try to encourage interested readers to follow and get involved in any of them by subscribing to the corresponding reflectors.

SI and TI Clarification

The VQEG No-Reference Metrics (NORM) group has recently focused on the topic of spatio-temporal complexity, revisiting the Spatial Information and Temporal Information (SI/TI) indicators, which are described in ITU-T Rec. P.910 [1]. They were originally developed for the T1A1 dataset in 1994 [2]. The metrics have found good use over the last 25 years – mostly employed for checking the complexity of video sources in datasets. However, SI/TI definitions contain ambiguities, so the goal of this sub-project is to provide revised definitions eliminating implementation inconsistencies.

Three main topics are discussed by VQEG in a series of online meetings:

• Comparison of existing publicly available implementations for SI/TI: a comparison was made between several public open-source implementations for SI/TI, based on initial feedback from members of Facebook. Bugs and inconsistencies were identified with the handling of video frame borders, treatment of limited vs. full range content, as well as the reporting of TI values for the first frame. Also, the lack of standardized test vectors was brought up as an issue. As a consequence, a new reference library was developed in Python by members of TU Ilmenau, incorporating all bug fixes that were previously identified, and introducing a new test suite, to which the public is invited to contribute material. VQEG is now actively looking for specific test sequences that will be useful for both validating existing SI/TI implementations, but also extending the scope of the metrics, which is related to the next issue described below.
• Study on how to apply SI/TI on different content formats: the description of SI/TI was found to be not suitable for extended applications such as video with a higher bit depth (> 8 Bit), HDR content, or spherical/3D video. Also, the question was raised on how to deal with the presence of scene changes in content. The community concluded that for content with higher bit depth, SI/TI functions should be calculated as specified, but that the output values could be mapped back to the original 8-Bit range to simplify comparisons. As for HDR, no conclusion was reached, given the inherent complexity of the subject. It was also preliminarily concluded that the treatment of scene changes should not be part of an SI/TI recommendation, instead focusing on calculating SI/TI for short sequences without scene changes, since the way scene changes would be dealt with may depend on the final application of the metrics.
• Discussion on other relevant uses of SI/TI: it has been widely used for checking video datasets in terms of diversity and classifying content. Also, SI/TI have been used in some no-reference metrics as content features. The question was raised whether SI/TI could be used for predicting how well content could be encoded. The group noted that different encoders would deal with sources differently, e.g. related to noise in the video. It was stated that it would be nice to be able to find a metric that was purely related to content and not affected by encoding or representation.

As a first step, this revision of the topic of SI/TI has resulted in a harmonized implementation and in the identification of future application areas. Discussions on these topics will continue in the next months through audio-calls that are open to interested readers.

Video Quality Metadata Standard

Also within NORM group, another topic was launched related to the inclusion of video quality metadata in compressed streams [3].

Almost all modern transcoding pipelines use full-reference video quality metrics to decide on the most appropriate encoding settings. The computation of these quality metrics is demanding in terms of time and computational resources. In addition, estimation errors propagate and accumulate when quality metrics are recomputed several times along the transcoding pipeline. Thus, retaining the results of these metrics with the video can alleviate these constraints, requiring very little space and providing a “greener” way of estimating video quality. With this goal, the new sub-project has started working towards the definition of a standard format to include video quality metrics metadata both at video bitstream level and system layer [4].

In this sense, the experts involved in the new sub-project are working on the following items:

• Identification of existing proposals and working groups within other standardisation bodies and organisations that address similar topics and propose amendments including new requirements. For example, MPEG has already worked on the adding of video quality metrics (e.g., PSNR, SSIM, MS-SSIM, VQM, PEVQ, MOS, FISG) metadata at system level (e.g, in MPEG2 streams [5], HTTP [6], etc.[7]).
• Identification of quality metrics to be considered in the standard. In principle, validated and standardized metrics are of interest, although other metrics can be also considered after a validation process on a standard set of subjective data (e.g., using existing datasets). New metrics to those used in previous approaches are of special interest. (e.g., VMAF [8], FB-MOS [9]).
• Consideration of the computation of multiple generations of full-reference metrics at different steps of the transcoding chain, of the use of metrics at different resolutions, different spatio-temporal aggregation methods, etc.
• Definition of a standard video quality metadata payload, including relevant fields such as metric name (e.g., “SSIM”), version (e.g., “v0.6.1”), raw score (e.g., “0.9256”), mapped-to-MOS score (e.g., “3.89”), scaling method (e.g., “Lanczos-5”), temporal reference (e.g., “0-3” frames), aggregation method (e.g., “arithmetic mean”), etc [4].

More details and information on how to join this activity can be found in the NORM webpage.

QoE metrics for live video streaming applications

The VQEG Audiovisual HD Quality (AVHD) group launched a new sub-project on QoE metrics for live media streaming applications (Live QoE) in the last VQEG meeting [10].

The success of a live multimedia streaming session is defined by the experience of a participating audience. Both the content communicated by the media and the quality at which it is delivered matter – for the same content, the quality delivered to the viewer is a differentiating factor. Live media streaming systems undertake a lot of investment and operate under very tight service availability and latency constraints to support multimedia sessions for their audience. Both to measure the return on investment and to make sound investment decisions, it is paramount that we be able to measure the media quality offered by these systems. In this sense, given the large scale and complexity of media streaming systems, objective metrics are needed to measure QoE.

Therefore, the following topics have been identified and are studied [11]:

• Creation of a high quality dataset, including media clips and subjective scores, which will be used to tune, train and develop objective QoE metrics. This dataset should represent the conditions that take place in typical live media streaming situations, therefore conditions and impairments comprising audio and video tracks (independently and jointly) will be considered. In addition, this datasets should cover a diverse set of content categories, including premium contentes (e.g., sports, movies, concerts, etc.) and user generated content (e.g., music, gaming, real life content, etc.).
• Development of QoE objective metrics, especially focusing on no-reference or near-no-reference metrics, given the lack of access to the original video at various points in the live media streaming chain. Different types of models will be considered including signal-based (operate on the decoded signal), metadata-based (operate on available metadata, e.g. codecs, resolution, framerate, bitrate, etc.), bitstream-based (operate on the parsed bitstream), and hybrid models (combining signal and metadata) [12]. Also, machine-learning based models will be explored.

Certain challenges are envisioned to be faced when dealing with these two topics, such as separating “content” from “quality” (taking int account that content plays a big role on engagement and acceptability), spectrum expectations, role of network impairments and the collection of enough data to develop robust models [11]. Readers interested in joining this effort are encouraged to visit AVHD webpage for more details.

Implementer’s Guide to Video Quality Metrics

In the last meeting, a new dedicated group on Implementer’s Guide to Video Quality Metrics (IGVQM) was set up to work on introducing and provide guidelines on implementing objective video quality metrics to the video compression community.

During the development of new video coding standards, peak-signal-to-noise-ratio (PSNR) has been traditionally used as the main objective metric to determine which new coding tools to be adopted. It has been furthermore used to establish the bitrate savings that a new coding standard offers over its predecessor through the employment of the so-called “BD-rate” metric [13] that still relies on PSNR for measuring quality.

Although this choice was fully justified for the first image/video coding standards – JPEG (1992), MPEG1 (1994), MPEG2 (1996), JPEG2000 and even H.264/AVC (2004) – since there was simply no other alternative at that time, its continuing use for the development of H.265/HEVC (2013), VP9 (2013), AV1 (2018) and most recently EVC and VVC (2020) is questionable, given the rapid and continuous evolution of more perceptual image/video objective quality metrics, such as SSIM (2004) [14], MS-SSIM (2004) [15], and VMAF (2015) [8].

This project attempts to offer some guidance to the video coding community, including standards setting organisations, on how to better utilise existing objective video quality metrics to better capture the improvements offered by video coding tools. For this, the following goals have been envisioned:

• Address video compression and scaling impairments only.
• Explore and use “state-of-the-art” full-reference (pixel) objective metrics, examine applicability of no-reference objective metrics, and obtain reference implementations of them.
• Offer temporal aggregation methods of image quality metrics into video quality metrics.
• Present statistical analysis of existing subjective datasets, constraining them to compression and scaling artifacts.
• Highlight differences among objective metrics and use-cases. For example, in case of very small differences, which metric is more sensitive? Which quality range is better served by what metric?
• Offer standard logistic mappings of objective metrics to a normalised linear scale.

More details can be found in the working document that has been set up to launch the project [16] and on the VQEG website.

References

[1] ITU-T Rec. P.910. Subjective video quality assessment methods for multimedia applications, 2008.
[2] M. H. Pinson and A. Webster, “T1A1 Validation Test Database,” VQEG eLetter, vol. 1, no. 2, 2015.
[3] I. Katsavounidis, “Video quality metadata in compressed bitstreams”, Presentation in VQEG Meeting, Dec. 2020.
[4] I. Katsavounidis et al. “A case for embedding video quality metrics as metadata in compressed bitstreams”, working document, 2019.
[5] ISO/IEC 13818-1:2015/AMD 6:2016 Carriage of Quality Metadata in MPEG2 Streams.
[6] ISO/IEC 23009 Dynamic Adaptive Streaming over HTTP (DASH).
[7] ISO/IEC 23001-10, MPEG Systems Technologies – Part 10: Carriage of timed metadata metrics of media in ISO base media file format.
[8] Toward a practical perceptual video quality metric, Tech blog with VMAF’s open sourcing on Github, Jun. 6, 2016.
[9] S.L. Regunathan, H. Wang, Y. Zhang, Y. R. Liu, D. Wolstencroft, S. Reddy, C. Stejerean, S. Gandhi, M. Chen, P. Sethi, A, Puntambekar, M. Coward, I. Katsavounidis, “Efficient measurement of quality at scale in Facebook video ecosystem”, in Applications of Digital Image Processing XLIII, vol. 11510, p. 115100J, Aug. 2020.
[10] R. Puri, “On a QoE metric for live media streaming applications”, Presentation in VQEG Meeting, Dec. 2020.
[11] R. Puri and S. Satti, “On a QoE metric for live media streaming applications”, working document, Jan. 2021.
[12] A. Raake, S. Borer, S. Satti, J. Gustafsson, R.R.R. Rao, S. Medagli, P. List, S. Göring, D. Lindero, W. Robitza, G. Heikkilä, S. Broom, C. Schmidmer, B. Feiten, U. Wüstenhagen, T. Wittmann, M. Obermann, R. Bitto, “Multi-model standard for bitstream-, pixel-based and hybrid video quality assessment of UHD/4K: ITU-T P.1204” , IEEE Access, vol. 8, Oct. 2020.
[13] G. Bjøntegaard, “Calculation of Average PSNR Differences Between RD-Curves”, Document VCEG-M33, ITU-T SG 16/Q6, 13th VCEG Meet- ing, Austin, TX, USA, Apr. 2001.
[14] Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” in IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, April 2004.
[15] Z. Wang, E. P. Simoncelli and A. C. Bovik, “Multiscale structural similarity for image quality assessment,” The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003, Pacific Grove, CA, USA, 2003.
[16] I. Katsavounidis, “VQEG’s Implementer’s Guide to Video Quality Metrics (IGVQM) project ”, working document, 2021.

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 133rd MPEG meeting was once again held as an online meeting, and this time, kicked off with great news, that MPEG is one of the organizations honored as a 72nd Annual Technology & Engineering Emmy® Awards Recipient, specifically the MPEG Systems File Format Subgroup and its ISO Base Media File Format (ISOBMFF) et al.

The official press release can be found here and comprises the following items:

• 6th Emmy® Award for MPEG Technology: MPEG Systems File Format Subgroup wins Technology & Engineering Emmy® Award
• Essential Video Coding (EVC) verification test finalized
• MPEG issues a Call for Evidence on Video Coding for Machines
• Neural Network Compression for Multimedia Applications – MPEG calls for technologies for incremental coding of neural networks
• MPEG Systems reaches the first milestone for supporting Versatile Video Coding (VVC) and Essential Video Coding (EVC) in the Common Media Application Format (CMAF)
• MPEG Systems continuously enhances Dynamic Adaptive Streaming over HTTP (DASH)
• MPEG Systems reached the first milestone to carry event messages in tracks of the ISO Base Media File Format

In this report, I’d like to focus on ISOBMFF, EVC, CMAF, and DASH.

MPEG Systems File Format Subgroup wins Technology & Engineering Emmy® Award

MPEG is pleased to report that the File Format subgroup of MPEG Systems is being recognized this year by the National Academy for Television Arts and Sciences (NATAS) with a Technology & Engineering Emmy® for their 20 years of work on the ISO Base Media File Format (ISOBMFF). This format was first standardized in 1999 as part of the MPEG-4 Systems specification and is now in its 6th edition as ISO/IEC 14496-12. It has been used and adopted by many other specifications, e.g.:

• MP4 and 3GP file formats;
• Carriage of NAL unit structured video in the ISO Base Media File Format which provides support for AVC, HEVC, VVC, EVC, and probably soon LCEVC;
• MPEG-21 file format;
• Dynamic Adaptive Streaming over HTTP (DASH) and Common Media Application Format (CMAF);
• High-Efficiency Image Format (HEIF);
• Timed text and other visual overlays in ISOBMFF;
• Common encryption format;
• Carriage of timed metadata metrics of media;
• Derived visual tracks;
• Event message track format;
• Carriage of uncompressed video;
• Omnidirectional Media Format (OMAF);
• Carriage of visual volumetric video-based coding data;
• Carriage of geometry-based point cloud compression data;
• … to be continued!

This is MPEG’s fourth Technology & Engineering Emmy® Award (after MPEG-1 and MPEG-2 together with JPEG in 1996, Advanced Video Coding (AVC) in 2008, and MPEG-2 Transport Stream in 2013) and sixth overall Emmy® Award including the Primetime Engineering Emmy® Awards for Advanced Video Coding (AVC) High Profile in 2008 and High-Efficiency Video Coding (HEVC) in 2017, respectively.

Essential Video Coding (EVC) verification test finalized

At the 133rd MPEG meeting, a verification testing assessment of the Essential Video Coding (EVC) standard was completed. The first part of the EVC verification test using high dynamic range (HDR) and wide color gamut (WCG) was completed at the 132nd MPEG meeting. A subjective quality evaluation was conducted comparing the EVC Main profile to the HEVC Main 10 profile and the EVC Baseline profile to AVC High 10 profile, respectively:

• Analysis of the subjective test results showed that the average bitrate savings for EVC Main profile are approximately 40% compared to HEVC Main 10 profile, using UHD and HD SDR content encoded in both random access and low delay configurations.
• The average bitrate savings for the EVC Baseline profile compared to the AVC High 10 profile is approximately 40% using UHD SDR content encoded in the random-access configuration and approximately 35% using HD SDR content encoded in the low delay configuration.
• Verification test results using HDR content had shown average bitrate savings for EVC Main profile of approximately 35% compared to HEVC Main 10 profile.

By providing significantly improved compression efficiency compared to HEVC and earlier video coding standards while encouraging the timely publication of licensing terms, the MPEG-5 EVC standard is expected to meet the market needs of emerging delivery protocols and networks, such as 5G, enabling the delivery of high-quality video services to an ever-growing audience. 

In addition to verification tests, EVC, along with VVC and CMAF were subject to further improvements to their support systems.

Research aspects: as for every new video codec, its compression efficiency and computational complexity are important performance metrics. Additionally, the availability of (efficient) open-source implementations (i.e., x264, x265, soon x266, VVenC, aomenc, et al., etc.) are vital for its adoption in the (academic) research community.

MPEG Systems reaches the first milestone for supporting Versatile Video Coding (VVC) and Essential Video Coding (EVC) in the Common Media Application Format (CMAF)

At the 133rd MPEG meeting, MPEG Systems promoted Amendment 2 of the Common Media Application Format (CMAF) to Committee Draft Amendment (CDAM) status, the first major milestone in the ISO/IEC approval process. This amendment defines:

• constraints to (i) Versatile Video Coding (VVC) and (ii) Essential Video Coding (EVC) video elementary streams when carried in a CMAF video track;
• codec parameters to be used for CMAF switching sets with VVC and EVC tracks; and
• support of the newly introduced MPEG-H 3D Audio profile.

It is expected to reach its final milestone in early 2022. For research aspects related to CMAF, the reader is referred to the next section about DASH.

MPEG Systems continuously enhances Dynamic Adaptive Streaming over HTTP (DASH)

At the 133rd MPEG meeting, MPEG Systems promoted Part 8 of Dynamic Adaptive Streaming over HTTP (DASH) also referred to as “Session-based DASH” to its final stage of standardization (i.e., Final Draft International Standard (FDIS)).

Historically, in DASH, every client uses the same Media Presentation Description (MPD), as it best serves the scalability of the service. However, there have been increasing requests from the industry to enable customized manifests for enabling personalized services. MPEG Systems has standardized a solution to this problem without sacrificing scalability. Session-based DASH adds a mechanism to the MPD to refer to another document, called Session-based Description (SBD), which allows per-session information. The DASH client can use this information (i.e., variables and their values) provided in the SBD to derive the URLs for HTTP GET requests.

An updated overview of DASH standards/features can be found in the Figure below.

Research aspects: CMAF is mostly like becoming the main segment format to be used in the context of HTTP adaptive streaming (HAS) and, thus, also DASH (hence also the name common media application format). Supporting a plethora of media coding formats will inevitably result in a multi-codec dilemma to be addressed in the near future as there will be no flag day where everyone will switch to a new coding format. Thus, designing efficient bitrate ladders for multi-codec delivery will an interesting research aspect, which needs to include device/player support (i.e., some devices/player will support only a subset of available codecs), storage capacity/costs within the cloud as well as within the delivery network, and network distribution capacity/costs (i.e., CDN costs).

The 134th MPEG meeting will be again an online meeting in April 2021. Click here for more information about MPEG meetings and their developments.

JPEG AI becomes a new work item of ISO/IEC

The 90th JPEG meeting was held online from 18 to 22 January 2021. This meeting was distinguished by very relevant activities, notably the new JPEG AI standardization project planning, and the analysis of the Call for Evidence on JPEG Pleno Point Cloud Coding.

The new JPEG AI Learning-based Image Coding System has become an official new work item registered under ISO/IEC 6048 and aims at providing compression efficiency in addition to image processing and computer visions tasks without the need for decompression.

The response to the Call for Evidence on JPEG Pleno Point Cloud Coding was a learning-based method that was found to offer state of the art compression efficiency.  Considering this response, the JPEG Pleno Point Cloud activity will analyse the possibility of preparing a future call for proposals on learning-based coding solutions that will also consider new functionalities, building on the relevant use cases already identified that require machine learning tasks processed in the compressed domain.

Meanwhile the new JPEG XL coding system has reached FDIS stage and it is ready for adoption. JPEG XL offers compression efficiency similar to the best state of the art in image coding, the best lossless compression performance, affordable low complexity and integration with the legacy JPEG image coding standard allowing a friendly transition between the two standards.

The 90^th JPEG meeting had the following highlights:

• JPEG AI,
• JPEG Pleno Point Cloud response to the Call for Evidence,
• JPEG XL Core Coding System reaches FDIS stage,
• JPEG Fake Media exploration,
• JPEG DNA continues the exploration on image coding suitable for DNA storage,
• JPEG systems,
• JPEG XS 2nd edition of Profiles reaches DIS stage.

JPEG AI

The scope of the JPEG AI is the creation of a learning-based image coding standard offering a single-stream, compact compressed domain representation, targeting both human visualization with significant compression efficiency improvement over image coding standards in common use at equivalent subjective quality, and effective performance for image processing and computer vision tasks, with the goal of supporting a royalty-free baseline.

JPEG AI has made several advances during the 90th technical meeting. During this meeting, the JPEG AI Use Cases and Requirements were discussed and collaboratively defined. Moreover, the JPEG AI vision and the overall system framework of an image compression solution with efficient compressed domain representation was defined. Following this approach, a set of exploration experiments were defined to assess the capabilities of the compressed representation generated by learning-based image codecs, considering some specific computer vision and image processing tasks.

Moreover, the performance assessment of the most popular objective quality metrics, using subjective scores obtained during the call for evidence were discussed, as well as anchors and some techniques to perform spatial prediction and entropy coding.

JPEG Pleno Point Cloud response to the Call for Evidence

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 including computer-aided manufacturing, entertainment, cultural heritage preservation, scientific research and advanced sensing and analysis. During the 90th JPEG meeting, the JPEG Committee reached an exciting major milestone and reviewed the results of its Final Call for Evidence on JPEG Pleno Point Cloud Coding. With an innovative Deep Learning based point cloud codec supporting scalability and random access submitted, the Call for Evidence results highlighted the emerging role of Deep Learning in point cloud representation and processing. Between the 90th and 91st meetings, the JPEG Committee will be refining the scope and direction of this activity in light of the results of the Call for Evidence.

JPEG XL Core Coding System reaches FDIS stage

The JPEG Committee has finalized JPEG XL Part 1 (Core Coding System), which is now at FDIS stage. The committee has defined new core experiments to determine appropriate profiles and levels for the codec, as well as appropriate criteria for defining conformance. With Part 1 complete, and Part 2 close to completion, JPEG XL is ready for evaluation and adoption by the market.

JPEG Fake Media exploration

The JPEG Committee initiated the JPEG Fake Media JPEG exploration study with the objective to create a standard that can facilitate secure and reliable annotation of media asset generation and modifications. The initiative aims to support usage scenarios that are in good faith as well as those with malicious intent. During the 90th JPEG meeting, the committee released a new version of the document entitled “JPEG Fake Media: Context Use Cases and Requirements” which is available on the JPEG website. A first workshop on the topic was organized on the 15th of December 2020. The program, presentations and a video recording of this workshop are available on the JPEG website. A second workshop will be organized around March 2021. More details will be made available soon on JPEG.org. JPEG invites interested parties to regularly visit https://jpeg.org/jpegfakemedia for the latest information and subscribe to the mailing list via http://listregistration.jpeg.org.

JPEG DNA continues the exploration on image coding suitable for DNA storage

The JPEG Committee continued its exploration for coding of images in quaternary representation, particularly suitable for DNA storage. After a second successful workshop presentation by stakeholders, additional requirements were identified, and a new version of the JPEG DNA overview document was issued and made publicly available. It was decided to continue this exploration by organising a third workshop and further outreach to stakeholders, as well as a proposal for an updated version of the JPEG overview document. Interested parties are invited to refer to the following URL and to consider joining the effort by registering to the mailing list of JPEG DNA here: https://jpeg.org/jpegdna/index.html.

JPEG Systems

JUMBF (ISO/IEC 19566-5) Amendment 1 draft review is complete and it is proceeding to international standard and subsequent publication; additional features to support new applications are under consideration.   Likewise, JPEG 360 (ISO/IEC 19566-5) Amendment 1 draft review is complete, and it is proceeding to international standard and subsequent publication.  The JLINK (ISO/IEC 19566-7) standard completed the committee draft review and is preparing a DIS study text ahead of the 91st meeting. The JPEG Snack (ISO/IEC 19566-8) will make a second working draft.  Interested parties can subscribe to the mailing list of the JPEG Systems AHG in order to contribute to the above activities.

JPEG XS 2nd edition of Profiles reaches DIS stage

The 2nd edition of Part 2 (Profiles) is now at the DIS stage and defines the required new profiles and levels to support the compression of raw Bayer content, mathematically lossless coding of up to 12-bit per component images, and 4:2:0 sampled image content. With the second editions of Parts 1, 2, and 3 completed, and the scheduled second editions of Part 4 (Conformance) and 5 (Reference Software), JPEG XS will soon have received a complete backwards-compatible revision of its entire suite of standards. Moreover, the committee defined a new exploration study to create new coding tools for improving the HDR and mathematically lossless compression capabilities, while still honoring the low-complexity and low-latency requirements.

Final Quote

“The official approval of JPEG AI by JPEG Parent Bodies ISO and IEC is a strong signal of support of this activity and its importance in the creation of AI-based imaging applications” said Prof. Touradj Ebrahimi, the Convenor of the JPEG Committee.

Future JPEG meetings are planned as follows:

• No 91, will be held online from April 19 to 23, 2021.
• No 92, will be held online from July 7 to 13, 2021.