针对体育视频设计感知加权质量评估方法，提升压缩效率。
Perceptually-Weighted Video Quality Metric for Asymmetric Encoded Sports Videos
- 基于多尺度小波和视觉显著性区分前景背景区域
- 在体育视频上实现0.9511的斯皮尔曼相关系数
- 适合用于非均匀编码内容的质量评估
客观视频质量评估通常假设空间注意力均匀，这与人类视觉选择性感知相悖，尤其在体育视频中。通过语义编码为显著区域分配更多比特，可显著节省码率。本文提出感知加权视频质量度量（PW-VQM），一种全参考度量，考虑空间区域的不均衡感知重要性，专为非对称编码内容设计。在多尺度小波域计算的SSIM图通过区分前景与背景区域进行加权。感知显著的前景区域由开放词汇目标检测结合光流分析识别，并在质量聚合时赋予更高权重。在体育视频内容上的评估显示，PW-VQM达到0.9511的斯皮尔曼等级相关系数，优于传统指标如SSIM、VMAF、FUNQUE和LPIPS。消融实验验证了各组件对感知加权的独立贡献。
原文摘要 · Abstract (English)
Objective video quality metrics commonly assume uniform spatial attention, an assumption that conflicts with the selective nature of human visual perception, particularly in sports videos. Here, allocating more bits for salient regions through semantic encoding can lead to significant bitrate savings. We present a Perceptually-Weighted Video Quality Metric (PW-VQM), a full-reference metric that accounts for the unequal perceptual importance of spatial regions and therefore targets quality evaluation for asymmetrically encoded content. SSIM maps computed in a multiscale wavelet domain are weighted by differentiating between foreground and background regions. Perceptually salient foreground regions are identified by combining open-vocabulary object detection with optical flow analysis, and are assigned higher weight during quality aggregation. Evaluated on sports video content, PW-VQM achieves a Spearman Rank Order Correlation Coefficient of 0.9511, outperforming established metrics including SSIM, VMAF, FUNQUE, and LPIPS. An ablation study confirms the individual contributions of the components of the perceptual weighting.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。