Quality Enhancement for Intra Frame Coding Via Cnns: An Adversarial Approach

Abstract

Lossy compression is an indispensable technique in image/video processing, due to its highly desirable ability of reducing the huge data volume. However, lossy compression introduces complex compression artifacts. To reduce these artifacts, post-processing techniques have been extensively studied. In this paper, we propose a novel post-processing technique using multi-level progressive refinement network via an adversarial training approach, called MPRGAN, for artifacts reduction and coding efficiency improvement in intra frame coding. Furthermore, our network generates multi-level residues in one feed-forward pass through the progressive reconstruction. This coarse-to-fine work fashion, which makes our network have high flexibility, can make trade-off between enhanced quality and computational complexity. Thereby facilitates the resource-aware applications. Extensive evaluations on benchmark datasets verify the superiority of our proposed MPRGAN model over the latest state-of-the-art methods with fast deployment running speed.