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KSVD-Based Multiple Description Image Coding
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DOI：10.1109/ACCESS.2018.2886823.png)
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Abstract
En 中文
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.
Keywords:
K singular value decomposition (KSVD)
multiple description coding
sparse representation
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