Advances in Quality Assessment Of Video Streaming Systems: Algorithms, Methods, Tools
Page 7371
Abstract
Quality assessment of video has matured significantly in the last 10 years due to a flurry of relevant developments in academia and industry, with relevant initiatives in VQEG, AOMedia, MPEG, ITU-T P.910, and other standardization and advisory bodies . Most advanced video streaming systems are now clearly moving away from good old-fashioned' PSNR and structural similarity type of assessment towards metrics that align better to mean opinion scores from viewers. Several of these algorithms, methods and tools have only been developed in the last 3-5 years and, while they are of significant interest to the research community, their advantages and limitations are not widely known in the research community. This tutorial provides this overview, but also focuses on practical aspects and how to design quality assessment tests that can scale to large datasets.
References
[1]
Eirina Bourtsoulatze, Aaron Chadha, Ilya Fadeev, Vasileios Giotsas, and Yiannis Andreopoulos. 2019. Deep video precoding. IEEE Transactions on Circuits and Systems for Video Technology 30, 12 (2019), 4913--4928.
[2]
Aaron Chadha and Yiannis Andreopoulos. 2021. Deep perceptual preprocessing for video coding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14852--14861.
[3]
Vasileios Giotsas, Nikos Deligiannis, Pam Fisher, and Yiannis Andreopoulos. 2015. Perceptual video quality estimation by regression with myopic experts. In 2015 Seventh International Workshop on Quality of Multimedia Experience (QoMEX). IEEE, 1--6.
[4]
Xiongkuo Min, Ke Gu, Guangtao Zhai, Xiaokang Yang, Wenjun Zhang, Patrick Le Callet, and ChangWen Chen. 2021. Screen content quality assessment: overview, benchmark, and beyond. ACM Computing Surveys (CSUR) 54, 9 (2021), 1--36.
[5]
Shankar L Regunathan, Haixiong Wang, Yun Zhang, Yu Ryan Liu, David Wolstencroft, Srinath Reddy, Cosmin Stejerean, Sonal Gandhi, Minchuan Chen, Pankaj Sethi, et al. 2020. Efficient measurement of quality at scale in Facebook video ecosystem. In Applications of Digital Image Processing XLIII, Vol. 11510. SPIE, 69--80.
[6]
Michela Testolina and Touradj Ebrahimi. 2021. Review of subjective quality assessment methodologies and standards for compressed images evaluation. In Applications of Digital Image Processing XLIV, Vol. 11842. SPIE, 302--315.
[7]
Abhinau K Venkataramanan, Cosmin Stejerean, and Alan C Bovik. 2022. FUNQUE: Fusion of Unified Quality Evaluators. arXiv preprint arXiv:2202.11241 (2022).
[8]
TAN Yaya and KONG Guangqian. 2021. Review of Research on Video Quality Assessment Based on Deep Learning. Journal of Frontiers of Computer Science & Technology 15, 3 (2021), 423.
Index Terms
- Advances in Quality Assessment Of Video Streaming Systems: Algorithms, Methods, Tools
Recommendations
Domain-Specific Fusion Of Objective Video Quality Metrics
MM '22: Proceedings of the 30th ACM International Conference on MultimediaVideo processing algorithms like video upscaling, denoising, and compression are now increasingly optimized for perceptual quality metrics instead of signal distortion. This means that they may score well for metrics like video multi-method assessment ...
Wireless Video Quality Assessment: A Study of Subjective Scores and Objective Algorithms
Evaluating the perceptual quality of video is of tremendous importance in the design and optimization of wireless video processing and transmission systems. In an endeavor to emulate human perception of quality, various objective video quality ...
An Evaluation of Video Quality Assessment Metrics for Passive Gaming Video Streaming
PV '18: Proceedings of the 23rd Packet Video WorkshopVideo Quality assessment is imperative to estimate and hence manage the Quality of Experience (QoE) in video streaming applications to the end-user. Recent years have seen a tremendous advancement in the field of objective video quality assessment (VQA) ...
Comments
Information & Contributors
Information
Published In
October 2022
7537 pages
ISBN:9781450392037
DOI:10.1145/3503161
- General Chairs:
- João Magalhães,
- Alberto del Bimbo,
- Shin'ichi Satoh,
- Nicu Sebe,
- Program Chairs:
- Xavier Alameda-Pineda,
- Qin Jin,
- Vincent Oria,
- Laura Toni
Copyright © 2022 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 10 October 2022
Check for updates
Author Tags
Qualifiers
- Abstract
Conference
MM '22
Sponsor:
Acceptance Rates
Overall Acceptance Rate 2,145 of 8,556 submissions, 25%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 98Total Downloads
- Downloads (Last 12 months)12
- Downloads (Last 6 weeks)3
Reflects downloads up to 10 Nov 2024
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in