Presentation | 2007-10-18 Image compression using nonlinear prediction by RBF networks and DPCM Keisuke NARISAWA, Takashi HOSHINO, Tohru IKEGUCHI, |
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Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | Differential pulse code modulation (DPCM) is one of the most famous methods to realize lossless image compression. Due to strong autocorrelation structures in image data, the DPCM reduces redundancy of information in the image data and exhibits good compression performance. However, the prediction accuracy of the DPCM becomes worse around edges of the image because the DPCM uses a linear function to predict pixel values. To avoid such worse situation, we propose a new method which combines a radial basis function (RBF) network and the DPCM. The RBF network is a nonlinear prediction method. As a result, the proposed method shows higher prediction accuracy and compression rate than the original DPCM. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | DPCM / RBF / image compression / nonlinear prediction |
Paper # | CAS2007-39,NLP2007-67 |
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Committee | NLP |
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Conference Date | 2007/10/11(1days) |
Place (in Japanese) | (See Japanese page) |
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Paper Information | |
Registration To | Nonlinear Problems (NLP) |
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Language | ENG |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Image compression using nonlinear prediction by RBF networks and DPCM |
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Keyword(1) | DPCM |
Keyword(2) | RBF |
Keyword(3) | image compression |
Keyword(4) | nonlinear prediction |
1st Author's Name | Keisuke NARISAWA |
1st Author's Affiliation | Graduate School of Science and Engineering, Saitama University() |
2nd Author's Name | Takashi HOSHINO |
2nd Author's Affiliation | Graduate School of Science and Engineering, Saitama University |
3rd Author's Name | Tohru IKEGUCHI |
3rd Author's Affiliation | Graduate School of Science and Engineering, Saitama University |
Date | 2007-10-18 |
Paper # | CAS2007-39,NLP2007-67 |
Volume (vol) | vol.107 |
Number (no) | 266 |
Page | pp.pp.- |
#Pages | 6 |
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