A Deep Learning post-processor with a perceptual loss function for video compression artifact removal
Darren Ramsook, Anil C. Kokaram, Neil Birkbeck, Y. Su, Balu Adsumilli · 2022
While video compression is necessary for large scale video streaming services, compression at low bitrate can degrade the original video and negatively affect the end user’s quality of experience. Deep Neural Networks (DNNs) are actively researched with respect to artifact removal, however the loss functions that are typically employed follows a derivation of a pixel-wise Lpnorm. In this paper we consider a DNN as a post-processor for video compression artifact removal. The DNN is trained using a composite perceptual loss that combines a traditional Lpnorm loss and a VMAF proxy network based on the Video Multimethod Assessment Function (VMAF). Results show an improvement in VMAF score over both the training and testing sets.