For Full-Text PDF, please login, if you are a member of IEICE,|
or go to Pay Per View on menu list, if you are a nonmember of IEICE.
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
Publication Date: 2018/10/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Image Processing and Video Processing
image coding, multiple description (MD) coding, convolutional neural network,
Full Text: PDF(1.1MB)>>
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.