KSVD-Based Multiple Description Image Coding

IEEE Access

Li Liu¹  Huaxiang Zhang¹  Guina Sun¹  Lili Meng¹  Yanyan Tan¹  Jia Zhang¹ 

[1] School of Information Science and Engineering, Shandong Normal University, Jinan, China;

关键词: K singular value decomposition (KSVD);    multiple description coding;    sparse representation;   

DOI  :  10.1109/ACCESS.2018.2886823

来源: DOAJ

【 摘 要 】

In this paper, we present a new multiple description coding scheme, which is based on a sparse dictionary training method called K singular value decomposition (KSVD). In the proposed scheme, each description encodes one source subset with a small quantization stepsize, and other subsets are predictively coded with a large quantization stepsize. The source processed by the KSVD becomes sparse, which can improve the coding efficiency. The proposed scheme is then applied to lapped transform-based multiple description image coding. Finally, image coding results show that the proposed scheme achieves a better performance than the current state-of-the-art multiple description coding methods.

【 授权许可】

Unknown