Citation

See, John Su Yang and Lin, Weiyao and He, Xiaoyi and Han, Xintong and Liu, Dong and Zou, Junni and Xiong, Hongkai and Wu, Feng (2020) Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC. IEEE Transactions on Multimedia, 22 (11). pp. 2749-2763. ISSN 1520-9210, 1941-0077

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Abstract

This article addresses neural network based post-processing for the state-of-the-art video coding standard, High Efficiency Video Coding (HEVC). We first propose a partition-aware convolution neural network (CNN) that utilizes the partition information produced by the encoder to assist in the post-processing. In contrast to existing CNN-based approaches, which only take the decoded frame as input, the proposed approach considers the coding unit (CU) size information and combines it with the distorted decoded frame such that the artifacts introduced by HEVC are efficiently reduced. We further introduce an adaptive-switching neural network (ASN) that consists of multiple independent CNNs to adaptively handle the variations in content and distortion within compressed-video frames, providing further reduction in visual artifacts. Additionally, an iterative training procedure is proposed to train these independent CNNs attentively on different local patch-wise classes. Experiments on benchmark sequences demonstrate the effectiveness of our partition-aware and adaptive-switching neural networks.

Item Type:	Article
Uncontrolled Keywords:	Convolutional neural network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Suzilawati Abu Samah
Date Deposited:	02 Nov 2021 01:50
Last Modified:	02 Nov 2021 01:50
URII:	http://shdl.mmu.edu.my/id/eprint/8406

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