论文标题
视频压缩数据集和基于学习的视频质量指标的基准
Video compression dataset and benchmark of learning-based video-quality metrics
论文作者
论文摘要
视频质量测量是视频处理中的关键任务。如今，许多新的编码标准的实现（例如AV1，VVC和LCEVC）都使用基于深度学习的解码算法，这些解码算法具有可感知指标，以作为优化目标。但是，对现代视频和图像质量指标的性能的调查通常采用使用旧标准（例如AVC）压缩的视频。在本文中，我们为评估视频压缩的视频质量指标提供了一个新的基准。它基于一个新的数据集，该数据集由大约2500个流的流，使用不同标准，包括AVC，HEVC，AV1，VP9和VVC。使用众包的成对比较来收集主观评分。评估指标的列表包括基于机器学习和神经网络的近期指标。结果表明，新的无参考指标与主观质量具有很高的相关性，并且接近了顶级全参考指标的能力。
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.
