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

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 qual...

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Bibliographic Details
Published inProceedings - International Conference on Image Processing pp. 216 - 220
Main Authors He, Xiaoyi, Hu, Qiang, Zhang, Xiaoyun, Zhang, Chongyang, Lin, Weiyao, Han, Xintong
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2018
Subjects
Convolutional neural network
Convolutional neural networks
Encoding
Feature extraction
High Efficiency Video Coding
Image coding
Quality enhancement
Training
Videos
Visualization
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Summary: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.
ISSN:2381-8549
DOI:10.1109/ICIP.2018.8451086