VQEG Column: VQEG Meeting May 2022

VQEG website: www.vqeg.org
Jesús Gutiérrez (jesus.gutierrez@upm.es), Universidad Politécnica de Madrid (Spain)
Kjell Brunnström (kjell.brunnstrom@ri.se), RISE (Sweden) 


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.

Group picture of the VQEG Meeting 9-13 May 2022 in Rennes (France).

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.


[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.

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