Perceptually motivated deep neural network for video compression artifact removal
Darren Ramsook, Anil C. Kokaram, Neil Birkbeck, Yeping Su, Balu Adsumilli · 2022
Recent advances have shown that latent representations of pre-trained Deep Convolutional Neural Networks (DCNNs) for classification can be modelled to generate scores that are well correlated with human perceptual judgement. In this paper we seek to extend the use of perceptually relevant losses in training a DCNN for video compression artefact removal. We will use internal representations of a pre-trained classification network as the basis of the loss functions. Specifically, the LPIPS metric and a perceptual discriminator will be responsible for low-level and high-level features respectively. The perceptual discriminator uses differing internal feature representations of the VGG network as its first stage of feature extraction. Initial results shows an increase in performance in perceptually based metrics such VMAF, LPIPS and BRISQUE, while showing a decrease in performance in PSNR.