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

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.

Automated summary

This paper proposes an in-loop filtering algorithm based on a dynamic convolutional capsule network, which adapt well to local features and improves the efficiency of video coding, and shows outstanding performance in terms of time efficiency.