Remote sensing image compression based on double-sparsity dictionary learning and universal trellis coded quantization
In this paper, we propose a novel remote sensing image compression method based on double-sparsity dictionary learning and universal trellis coded quantization (UTCQ). Recent years have seen a growing interest in the study of natural image compression based on sparse representation and dictionary le...
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| Vydáno v: | Proceedings - International Conference on Image Processing s. 1665 - 1669 |
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| Jazyk: | angličtina |
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01.09.2013
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| ISSN: | 1522-4880 |
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| Abstract | In this paper, we propose a novel remote sensing image compression method based on double-sparsity dictionary learning and universal trellis coded quantization (UTCQ). Recent years have seen a growing interest in the study of natural image compression based on sparse representation and dictionary learning. We show that using the double-sparsity model to learn a dictionary gives much better compression results for remote sensing images, the texture of which is much richer than that of natural images. We also show that the compression performance is improved significantly when advanced quantization and entropy coding strategies are used for encoding the sparse representation coefficients. The proposed method outperforms the existing dictionary-based image coding algorithms. Additionally, our method results in better ratedistortion performance and structural similarity results than CCSDS and JPEG2000 standard. |
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| AbstractList | In this paper, we propose a novel remote sensing image compression method based on double-sparsity dictionary learning and universal trellis coded quantization (UTCQ). Recent years have seen a growing interest in the study of natural image compression based on sparse representation and dictionary learning. We show that using the double-sparsity model to learn a dictionary gives much better compression results for remote sensing images, the texture of which is much richer than that of natural images. We also show that the compression performance is improved significantly when advanced quantization and entropy coding strategies are used for encoding the sparse representation coefficients. The proposed method outperforms the existing dictionary-based image coding algorithms. Additionally, our method results in better ratedistortion performance and structural similarity results than CCSDS and JPEG2000 standard. |
| Author | Zhan, Xin Hu, Anzhou Yin, Dong Zhang, Rong Hu, Wenlong |
| Author_xml | – sequence: 1 givenname: Xin surname: Zhan fullname: Zhan, Xin organization: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China – sequence: 2 givenname: Rong surname: Zhang fullname: Zhang, Rong organization: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China – sequence: 3 givenname: Dong surname: Yin fullname: Yin, Dong organization: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China – sequence: 4 givenname: Anzhou surname: Hu fullname: Hu, Anzhou organization: Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei 230027, China – sequence: 5 givenname: Wenlong surname: Hu fullname: Hu, Wenlong organization: Key Laboratory of Geospatial Information Processing and Application System Technology, Chinese Academy of Sciences, Beijing 100190, China |
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| SubjectTerms | Atoms Dictionaries Dictionary learning Image coding image compression Machine learning Quantization (signal) Remote sensing Sparse approximation Training Transform coding universal trellis coded quantization Vectors |
| Title | Remote sensing image compression based on double-sparsity dictionary learning and universal trellis coded quantization |
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