A differentiable estimator of VMAF for Video

Darren Ramsook, Anil C. Kokaram, Noel E. O'Connor, Neil Birkbeck, Yeping Su, Balu Adsumilli

Abstract: Modern Perceptual Visual Quality Metrics (PVQMs) for video are generally complex and non-differentiable. This makes them difficult to use as loss functions in restoration and compression tuning. Traditional metrics such as PSNR/MSE which are differentiable remain important but do not capture perceptual visual criteria. In this paper we present a DNN which models a popular perceptual video metric VMAF. In so doing, we introduce a differentiable loss function that closely matches the behaviour of a perceptual metric. Employing degradation generated with H.265 compression, our model achieves a 4.41% RMSE in predicting VMAF. This can now be deployed as a video based loss function in video enhancement and compression tasks.