为机器视觉优化压缩视频质量，提出新评估指标。
Machine vision-aware quality metrics for compressed image and video assessment
- 针对物体、人脸、车牌检测任务设计专用质量度量
- 新指标在机器视觉任务中与实际表现相关性更强
- 适合自动驾驶、安防等机器主导的视觉系统
视频压缩算法的核心目标是在控制文件大小的同时提升人眼感知的视觉质量。然而，在视频监控和自动驾驶等场景中，检测与识别类机器视觉任务处理海量数据，需尽量减少人工干预。此时，视频编解码器应针对机器视觉进行优化。本文研究压缩对检测与识别算法（包括物体、人脸、车牌）的影响，并为每项任务提出新型全参考图像/视频质量度量，专用于机器视觉评估。实验结果表明，所提度量在对应任务上的表现与机器视觉结果的相关性优于现有通用质量度量。
原文摘要 · Abstract (English)
A main goal in developing video-compression algorithms is to enhance human-perceived visual quality while maintaining file size. But modern video-analysis efforts such as detection and recognition, which are integral to video surveillance and autonomous vehicles, involve so much data that they necessitate machine-vision processing with minimal human intervention. In such cases, the video codec must be optimized for machine vision. This paper explores the effects of compression on detection and recognition algorithms (objects, faces, and license plates) and introduces novel full-reference image/video-quality metrics for each task, tailored to machine vision. Experimental results indicate our proposed metrics correlate better with the machine-vision results for the respective tasks than do existing image/video-quality metrics.
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