• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Convolutional auto-encoder based multiple description coding network


Abstract

When data is transmitted over an unreliable channel, the error of the data packet may result in serious degradation. The multiple description coding (MDC) can solve this problem and save transmission costs. In this paper, we propose a deep multiple description coding network (MDCN) to realize efficient image compression. Firstly, our network framework is based on convolutional auto-encoder (CAE), which include multiple description encoder network (MDEN) and multiple description decoder network (MDDN). Secondly, in order to obtain high-quality reconstructed images at low bit rates, the encoding network and decoding network are integrated into an end-to-end compression framework. Thirdly, the multiple description decoder network includes side decoder network and central decoder network. When the decoder receives only one of the two multiple description code streams, side decoder network is used to obtain side reconstructed image of acceptable quality. When two descriptions are received, the high quality reconstructed image is obtained. In addition, instead of quantization with additive uniform noise, and SSIM loss and distance loss combine to train multiple description encoder networks to ensure that they can share structural information. Experimental results show that the proposed framework performs better than traditional multiple description coding methods.


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Cite this article

[IEEE Style]
L. Meng, H. Li, J. Zhang, Y. Tan, Y. Ren and H. Zhang, "Convolutional auto-encoder based multiple description coding network," KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1689-1703, 2020. DOI: 10.3837/tiis.2020.04.015.

[ACM Style]
Lili Meng, Hongfei Li, Jia Zhang, Yanyan Tan, Yuwei Ren, and Huaxiang Zhang. 2020. Convolutional auto-encoder based multiple description coding network. KSII Transactions on Internet and Information Systems, 14, 4, (2020), 1689-1703. DOI: 10.3837/tiis.2020.04.015.