标题： DIVA-VQA：检测UGC视频质量中的帧间变化 显示英文标题

标题： DIVA-VQA: Detecting Inter-frame Variations in UGC Video Quality

摘要： 用户生成（视频）内容（UGC）的快速增长推动了对无参考（NR）感知视频质量评估（VQA）的研究需求。 NR-VQA是社交媒体和流媒体应用中大规模视频质量监控的关键组成部分，在这些应用中无法获得原始参考。 本文提出了一种基于帧间变化驱动的时空碎片化的新颖NR-VQA模型。 通过利用这些帧间差异，该模型在多个层次上逐步分析质量敏感区域：帧、块和碎片化帧。 它整合了与残差对齐的帧、碎片化残差和碎片化帧，以有效捕捉全局和局部信息。 该模型提取二维和三维特征，以表征这些时空变化。 在五个UGC数据集上进行的实验以及与最先进模型的对比表明，我们的方法在平均等级相关性方面排名前二（DIVA-VQA-L: 0.898 和 DIVA-VQA-B: 0.886）。 该方法在低运行时复杂度下实现了改进的性能，与现有最快的NR-VQA方法相比，DIVA-VQA-B排名首位，DIVA-VQA-L平均排名第三。 代码和模型可在以下位置公开获取：https://github.com/xinyiW915/DIVA-VQA. 显示英文摘要

摘要： The rapid growth of user-generated (video) content (UGC) has driven increased demand for research on no-reference (NR) perceptual video quality assessment (VQA). NR-VQA is a key component for large-scale video quality monitoring in social media and streaming applications where a pristine reference is not available. This paper proposes a novel NR-VQA model based on spatio-temporal fragmentation driven by inter-frame variations. By leveraging these inter-frame differences, the model progressively analyses quality-sensitive regions at multiple levels: frames, patches, and fragmented frames. It integrates frames, fragmented residuals, and fragmented frames aligned with residuals to effectively capture global and local information. The model extracts both 2D and 3D features in order to characterize these spatio-temporal variations. Experiments conducted on five UGC datasets and against state-of-the-art models ranked our proposed method among the top 2 in terms of average rank correlation (DIVA-VQA-L: 0.898 and DIVA-VQA-B: 0.886). The improved performance is offered at a low runtime complexity, with DIVA-VQA-B ranked top and DIVA-VQA-L third on average compared to the fastest existing NR-VQA method. Code and models are publicly available at: https://github.com/xinyiW915/DIVA-VQA.