Video-quality measurement is a critical task in video processing. Nowadays, many implementations of new encoding standards - such as AV1, VVC, and LCEVC - use deep-learning-based decoding algorithms with perceptual metrics that serve as optimization objectives. But investigations of the performance of modern video- and image-quality metrics commonly employ videos compressed using older standards, such as AVC. In this paper, we present a new benchmark for video-quality metrics that evaluates video compression. It is based on a new dataset consisting of about 2,500 streams encoded using different standards, including AVC, HEVC, AV1, VP9, and VVC. Subjective scores were collected using crowdsourced pairwise comparisons. The list of evaluated metrics includes recent ones based on machine learning and neural networks. The results demonstrate that new no-reference metrics exhibit a high correlation with subjective quality and approach the capability of top full-reference metrics.

翻译：视频质量测量是视频处理中的关键任务。如今，许多新编码标准（如AV1、VVC和LCEVC）的实现采用基于深度学习的解码算法，并以感知度量作为优化目标。然而，现代视频和图像质量度量的性能研究通常使用较旧标准（如AVC）压缩的视频。本文提出了一种评估视频压缩的新视频质量度量基准，该基准基于包含约2,500个视频流的新数据集，这些视频流采用不同编码标准（包括AVC、HEVC、AV1、VP9和VVC）进行压缩。主观分数通过众包成对比较方法收集。所评估的度量列表包括基于机器学习与神经网络的最新方法。结果表明，新的无参考度量与主观质量具有高度相关性，且性能接近顶级全参考度量。