标题： 基于机器学习的视频编解码器的全参考视频质量评估 显示英文标题

标题： Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs

摘要： 基于机器学习的视频编解码器在过去几年中取得了显著进展。 开发基于机器学习的视频编解码器的一个关键领域是准确的评估指标，该指标不需要昂贵且耗时的主观测试。 我们表明，那些为基于数字信号处理（DSP）的视频编解码器设计和训练的评估指标，在用于基于机器学习的视频编解码器时，与主观意见的相关性不高，因为基于机器学习的视频编解码器和传统视频编解码器产生的视频伪影差异较大。 我们提供了一个新的基于机器学习的视频编解码器视频数据集，这些视频已针对质量进行了准确标注。 我们还提出了一种新的全参考视频质量评估（FRVQA）模型，在模型级别上实现了0.99的皮尔逊相关系数（PCC）和0.99的斯皮尔曼等级相关系数（SRCC）。 我们将数据集和FRVQA模型开源，以帮助加速基于机器学习的视频编解码器的研究，并使其他人能够进一步改进FRVQA模型。 显示英文摘要

摘要： Machine learning-based video codecs have made significant progress in the past few years. A critical area in the development of ML-based video codecs is an accurate evaluation metric that does not require an expensive and slow subjective test. We show that existing evaluation metrics that were designed and trained on DSP-based video codecs are not highly correlated to subjective opinion when used with ML video codecs due to the video artifacts being quite different between ML and video codecs. We provide a new dataset of ML video codec videos that have been accurately labeled for quality. We also propose a new full reference video quality assessment (FRVQA) model that achieves a Pearson Correlation Coefficient (PCC) of 0.99 and a Spearman's Rank Correlation Coefficient (SRCC) of 0.99 at the model level. We make the dataset and FRVQA model open source to help accelerate research in ML video codecs, and so that others can further improve the FRVQA model.