LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models
Summary
The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs). Existing video quality benchmarks predominantly focus on short clips and isolated distortions, overlooking the temporal continuity, cumu...
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arXiv:2607.01086v1 (cs)
[Submitted on 1 Jul 2026]
Title:LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models
Authors:Arpita Nema,Hanwei Zhu,Xi Zhang,Weisi Lin
View a PDF of the paper titled LongVQUBench: Benchmarking Long-Term Video Quality Understanding of Vision-Language Models, by Arpita Nema and 3 other authors
Abstract:The evaluation of long-term video quality understanding remains an open challenge for large vision-language models (LVLMs). Existing video quality benchmarks predominantly focus on short clips and isolated distortions, overlooking the temporal continuity, cumulative degradation, and reasoning complexity inherent in long-duration content. To address these limitations, we present LongVQUBench, a comprehensive benchmark for long-term video quality understanding. LongVQUBench contains over 1200 diverse videos spanning movies, documentaries, surveillance footage, egocentric recordings, and animated content, accompanied by 1500 multiple-choice and open-ended questions for validation and testing. To assess perceptual reasoning across different temporal scopes, we introduce three progressively complex evaluation levels: (i) local event quality understanding (LQU) for analyzing localized distortions; (ii) cross-event quality reasoning (CQR) for integrating multiple degraded events; and (iii) global quality understanding (GQU) for holistic perceptual evaluation over extended durations. Furthermore, a needle distortion question-answering (NDQA) paradigm is embedded across all three levels, where spatial or temporal artifacts are sparsely inserted to probe fine-grained detection and reasoning capabilities. Extensive experiments on 14 state-of-the-art LVLMs reveal significant performance degradation with increasing video length and reasoning depth, highlighting their limited capacity for long-range temporal integration and perceptual attribution. We envision LongVQUBench as a foundational step toward the systematic, hierarchical, and explainable evaluation of LVLMs' long-term video quality understanding.
| | | | --- | --- | | Comments: | Accepted at European Conference on Computer Vision 2026 | | Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) | | Cite as: |arXiv:2607.01086[cs.CV] | | | (orarXiv:2607.01086v1[cs.CV] for this version) | | |https://doi.org/10.48550/arXiv.2607.01086<br>Focus to learn more<br>arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Arpita Nema \[view email]
[v1] Wed, 1 Jul 2026 15:40:42 UTC (6,812 KB)
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