Standard-Compliant Multiple Description Image Coding Based on Convolutional Neural Networks

Ting ZHANG  Huihui BAI  Mengmeng ZHANG  Yao ZHAO  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.10   pp.2543-2546
Publication Date: 2018/10/01
Publicized: 2018/07/19
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8028
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
image coding,  multiple description (MD) coding,  convolutional neural network,  

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Multiple description (MD) coding is an attractive framework for robust information transmission over non-prioritized and unpredictable networks. In this paper, a novel MD image coding scheme is proposed based on convolutional neural networks (CNNs), which aims to improve the reconstructed quality of side and central decoders. For this purpose initially, a given image is encoded into two independent descriptions by sub-sampling. Such a design can make the proposed method compatible with the existing image coding standards. At the decoder, in order to achieve high-quality of side and central image reconstruction, three CNNs, including two side decoder sub-networks and one central decoder sub-network, are adopted into an end-to-end reconstruction framework. Experimental results show the improvement achieved by the proposed scheme in terms of both peak signal-to-noise ratio values and subjective quality. The proposed method demonstrates better rate central and side distortion performance.