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 f...

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Vydáno v:	IET image processing Ročník 17; číslo 2; s. 439 - 449
Hlavní autoři:	Su, LiChao, Cao, Mengqing, Yu, Yue, Chen, Jian, Yang, XiuZhi, Wu, Dapeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Wiley 01.02.2023
ISSN:	1751-9659, 1751-9667
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
ISSN:	1751-9659 1751-9667
DOI:	10.1049/ipr2.12644