Enhancing HEVC Compressed Videos with a Partition-masked Convolutional Neural Network

Abstract

In this paper, we propose a partition-masked Convolution Neural Network (CNN) to achieve compressed-video enhancement for the state-of-the-art coding standard, High Efficiency Video Coding (HECV). More precisely, our method utilizes the partition information produced by the encoder to guide the quality enhancement process. In contrast to existing CNN-based approaches, which only take the decoded frame as the input to the CNN, the proposed approach considers the coding unit (CU) size information and combines it with the distorted decoded frame such that the degradation introduced by HEVC is reduced more efficiently. Experimental results show that our approach leads to over 9.76% BD-rate saving on benchmark sequences, which achieves the state-of-the-art performance.

arXiv commentAccepted by ICIP 2018

References in corpus (1)

• A Convolutional Neural Network Approach for Post-Processing in HEVC Intra Coding

Cited by in corpus (6)

• A Comprehensive Benchmark for Single Image Compression Artifacts Reduction
• Partition-Aware Adaptive Switching Neural Networks for Post-Processing in HEVC
• Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
• Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering
• Deep Multi-modality Soft-decoding of Very Low Bit-rate Face Videos
• B-DRRN: A Block Information Constrained Deep Recursive Residual Network for Video Compression Artifacts Reduction