Abstract
Video Quality Assessment (VQA) is vital for large-scale video retrieval systems, aimed at identifying quality issues to prioritize high-quality videos. In industrial systems, low-quality video characteristics fall into four categories: visual-related issues like mosaics and black boxes, textual issues from video titles and OCR content, and semantic issues like frame incoherence and frame-text mismatch from AI-generated videos. Despite their prevalence in industrial settings, these low-quality videos have been largely overlooked in academic research, posing a challenge for accurate identification. To address this, we introduce the Multi-Branch Collaborative Network (MBCN) tailored for industrial video retrieval systems. MBCN features four branches, each designed to tackle one of the aforementioned quality issues. After each branch independently scores videos, we aggregate these scores using a weighted approach and a squeeze-and-excitation mechanism to dynamically address quality issues across different scenarios. We implement point-wise and pair-wise optimization objectives to ensure score stability and reasonableness. Extensive offline and online experiments on a world-level video search engine demonstrate MBCN's effectiveness in identifying video quality issues, significantly enhancing the retrieval system's ranking performance. Detailed experimental analyses confirm the positive contribution of all four evaluation branches. Furthermore, MBCN significantly improves recognition accuracy for low-quality AI-generated videos compared to the baseline.
Abstract (translated)
视频质量评估（VQA）对于大规模的视频检索系统至关重要，旨在识别质量问题以优先处理高质量视频。在工业系统中，低质量视频的特点主要分为四类：视觉相关问题如马赛克和黑屏、文字相关的从视频标题和OCR内容提取的问题、以及语义方面的问题比如帧间不一致性和帧与文本不符的情况（尤其是在由AI生成的视频中）。尽管这些低质量视频在工业环境中普遍存在，但在学术研究中却受到了很大程度上的忽视，这给准确识别带来了挑战。为了解决这一问题，我们引入了专门针对工业级视频检索系统的多分支协作网络（MBCN）。 MBCN包括四个分支，每个分支都设计用于解决上述一个质量评估问题。在每个分支独立打分后，我们会采用加权方式和挤压-激励机制来聚合这些分数，以便动态应对不同场景中的质量问题。为了确保评分的稳定性和合理性，我们实施了点对点和成对优化目标。 通过世界级别的视频搜索引擎进行广泛的离线和在线实验表明，MBCN在识别视频质量问题方面非常有效，并显著提升了检索系统的排序性能。详细的实验分析证实了所有四个评估分支的正面贡献。此外，与基线相比，MBCN大幅提高了低质量AI生成视频的识别准确率。 简而言之，通过引入MBCN并针对工业环境中的特定挑战进行优化，我们能够更有效地识别和处理大规模视频检索系统中的质量问题，从而提供更好的用户体验和搜索结果精度。
URL
https://arxiv.org/abs/2502.05924
