Full Reference Video Quality Assessment for Machine Learning-Based Video
  Codecs
Abrar Majeedi Babak Naderi Yasaman Hosseinkashi Juhee Cho Ruben Alvarez Martinez Ross Cutler
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.