Dictionary learning-based image compression

Dictionary learning based image compression has attracted a lot of research efforts due to the inherent sparsity of image contents. Most algorithms in the literature, however, suffer from two drawbacks. First, the atoms selected for image patch reconstruction scatter over the entire dictionary, whic...

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Veröffentlicht in:2017 IEEE International Conference on Image Processing (ICIP) S. 3235 - 3239
Hauptverfasser: Wang, Hao, Xia, Yong, Wang, Zhiyong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.09.2017
Schlagworte:
adaptive quantization
Approximation algorithms
Dictionaries
dictionary learning
Image coding
Image compression
Image reconstruction
information entropy
Quantization (signal)
Sparse matrices
Transform coding
ISSN:2381-8549
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Zusammenfassung:Dictionary learning based image compression has attracted a lot of research efforts due to the inherent sparsity of image contents. Most algorithms in the literature, however, suffer from two drawbacks. First, the atoms selected for image patch reconstruction scatter over the entire dictionary, which leads to a high coding cost. Second, the sparse representation of image patches is performed independently from the quantization of sparse coefficients, which may result in a sub-optimal solution. In this paper, we propose the entropy based orthogonal matching pursuit (EOMP) algorithm and quantization KSVD (QKSVD) algorithm for dictionary learning-based image compression. An entropy regularization term is utilized in EOMP to restrict atom selection, and hence reduces the coding cost, and an adaptive quantization method is incorporated into the dictionary learning procedure in QKSVD to minimize the reconstruction error and quantization error simultaneously. Experimental results on 10 standard benchmark images demonstrate that our proposed approach achieves better performance than several state-of-the-art ones at low bit rate, such as KSVD based compression approach, JPEG, and JPEG-2000.
ISSN:2381-8549
DOI:10.1109/ICIP.2017.8296880