Full Reference Video Quality Assessment for Machine Learning-Based Video Codecs
This addresses the need for reliable quality assessment in ML video codec development, which is incremental as it builds on existing FRVQA methods but targets a new domain.
The paper tackles the problem of inaccurate evaluation metrics for machine learning-based video codecs by introducing a new dataset and a full reference video quality assessment (FRVQA) model, achieving Pearson and Spearman correlation coefficients of 0.99.
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.