Dynamic convolutional capsule network for In‐loop filtering in HEVC video codec

Abstract

Abstract Recently, several in‐loop filtering algorithms based on convolutional neural network (CNN) have been proposed to improve the efficiency of HEVC (High Efficiency Video Coding). Conventional CNN‐based filters only apply a single model to the whole image, which cannot adapt well to all local features from the image. To solve this problem, an in‐loop filtering algorithm based on a dynamic convolutional capsule network (DCC‐net) is proposed, which embeds localized dynamic routing and dynamic segmentation algorithms into capsule network, and integrate them into the HEVC hybrid video coding framework as a new in‐loop filter. The proposed method brings average 7.9% and 5.9% BD‐BR reductions under all intra (AI) and random access (RA) configurations, respectively, as well as, 0.4 dB and 0.2 dB BD‐PSNR gains, respectively. In addition, the proposed algorithm has an outstanding performance in terms of time efficiency.