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Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach

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Weisi LinAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 1045 - 1054

https://doi.org/10.1145/3581783.3611737

Published: 27 October 2023 Publication History

Abstract

The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQA on in-the-wild videos is especially challenging as it could be affected by complicated factors, including various distortions and diverse contents. Though subjective studies have collected overall quality scores for these videos, how the abstract quality scores relate with specific factors is still obscure, hindering VQA methods from more concrete quality evaluations (e.g. sharpness of a video). To solve this problem, we collect over two million opinions on 4,543 in-the-wild videos on 13 dimensions of quality-related factors, including in-capture authentic distortions (e.g. motion blur, noise, flicker), errors introduced by compression and transmission, and higher-level experiences on semantic contents and aesthetic issues (e.g. composition, camera trajectory), to establish the multi-dimensional Maxwell database. Specifically, we ask the subjects to label among a positive, a negative, and a neutral choice for each dimension. These explanation-level opinions allow us to measure the relationships between specific quality factors and abstract subjective quality ratings, and to benchmark different categories of VQA algorithms on each dimension, so as to more comprehensively analyze their strengths and weaknesses. Furthermore, we propose the MaxVQA, a language-prompted VQA approach that modifies vision-language foundation model CLIP to better capture important quality issues as observed in our analyses. The MaxVQA can jointly evaluate various specific quality factors and final quality scores with state-of-the-art accuracy on all dimensions, and superb generalization ability on existing datasets. Code and data available at https://github.com/VQAssessment/MaxVQA.

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Cited By

Lin PLi RJi YYu ZFeng FMa ZWang XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Triple Alignment Strategies for Zero-shot Phrase Grounding under Weak SupervisionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680897(4312-4321)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680897
Xiang JDang YChen PLiang RHuan RGao NCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Semantic-Aware and Quality-Aware Interaction Network for Blind Video Quality AssessmentProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680598(9970-9979)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680598
Zhang AWang YTang WLi LKwong S(2024)A Spatial–Temporal Video Quality Assessment Method via Comprehensive HVS SimulationIEEE Transactions on Cybernetics10.1109/TCYB.2023.333861554:8(4749-4762)Online publication date: Aug-2024
https://doi.org/10.1109/TCYB.2023.3338615
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Index Terms

Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

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cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 Owner/Author.

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike International 4.0 License.

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Association for Computing Machinery

New York, NY, United States

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Published: 27 October 2023

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MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

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Lin PLi RJi YYu ZFeng FMa ZWang XCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Triple Alignment Strategies for Zero-shot Phrase Grounding under Weak SupervisionProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680897(4312-4321)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680897
Xiang JDang YChen PLiang RHuan RGao NCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Semantic-Aware and Quality-Aware Interaction Network for Blind Video Quality AssessmentProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680598(9970-9979)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680598
Zhang AWang YTang WLi LKwong S(2024)A Spatial–Temporal Video Quality Assessment Method via Comprehensive HVS SimulationIEEE Transactions on Cybernetics10.1109/TCYB.2023.333861554:8(4749-4762)Online publication date: Aug-2024
https://doi.org/10.1109/TCYB.2023.3338615
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Liu XMin XZhai GLi CKou TSun WWu HGao YCao YZhang ZWu XTimofte RPeng FFu HMing AWang CMa HHe SDou ZChen SZhang HXie HWang CChen BZeng JYang JWang WFang XLv XYan JZhi TZhang YLi YLi YXu JLiu JLiao YLi JYu ZGuan FLu YLi XMotamednia HHosseini-Benvidi SMahmoudi-Aznaveh AMansouri AGankhuyag GYoon KXu YFan HKong FZhao SDong WYin HZhu LWang ZHuang BSaha AMishra SGupta SSureddi RSaha OCelona LBianco SNapoletano PSchettini RYang JFu JZhang WCao WLiu LPeng HYuan WLi ZCheng YDeng YLi HQu BLi YLuo SWang SGao WLu ZConde MTimofte RWang XChen ZLiao RYe YWang QLi BZhou ZGeng MChen RTao XLiang XSun SMa XLi JYang MXu HZhou JZhu SYu BChen PXu XShen JDuan ZAsadi ELiu JYan QQu YZeng XWang LLiao R(2024)NTIRE 2024 Quality Assessment of AI-Generated Content Challenge2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)10.1109/CVPRW63382.2024.00637(6337-6362)Online publication date: 17-Jun-2024
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