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.

翻译：开发视频压缩算法的主要目标是在保持文件大小的同时提升人类感知的视觉质量。然而，现代视频分析任务（如检测与识别）作为视频监控和自动驾驶系统的核心组成部分，涉及海量数据，必须依赖机器视觉处理且需尽量减少人工干预。在此类场景中，视频编解码器必须针对机器视觉进行优化。本文探究了压缩对检测与识别算法（包括物体、人脸及车牌识别）的影响，并针对机器视觉需求，为各项任务提出了全新的全参考图像/视频质量评估指标。实验结果表明，相较于现有图像/视频质量指标，我们提出的指标在相应任务中与机器视觉结果具有更高的相关性。