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Your search keyword '"Li, Yuancheng"' showing total 20 results
Start Over Author "Li, Yuancheng" Publication Type Books
20 results on '"Li, Yuancheng"'

19. Image Digital Watermarking Technique Based on Kernel Independent Component Analysis.

Author

Carbonell, Jaime G., Siekmann, Jörg, An, Aijun, Stefanowski, Jerzy, Ramanna, Sheela, Butz, Cory J., Pedrycz, Witold, Wang, Guoyin, Li, Yuancheng, Wu, Kehe, Ma, Yinglong, and Zhang, Shipeng

Abstract

In this paper, we present a novel image digital watermarking technique based on Kernel Independent Component Analysis (KICA). Use the nice characteristic of the KICA, which can results the blind separation of nonlinearly mixed signals, the imperceptibility and robustness requirements of watermarks are fulfilled and optimized. In the proposed scheme, the watermark image is first transformed by Arnold method, and then embedded into the lowest frequency subband in DWT domain. The recovery of owner's image is turning the watermarked image into DWT domains then use KICA to extract the watermark. Finally the watermark is transformed by Arnold method again, so we can get the original watermark image. Experimental results show that the proposed method gains better performance in robustness than that of ICA with respect to traditional image processing including cropping, filtering, add noise and JPEG image compression. [ABSTRACT FROM AUTHOR]

Published
2007
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20. SVM Regression and Its Application to Image Compression.

Author

Huang, De-Shuang, Zhang, Xiao-Ping, Huang, Guang-Bin, Jiao, Runhai, Li, Yuancheng, Wang, Qingyuan, and Li, Bo

Abstract

This paper proposes a new image compression algorithm which combines SVM regression with wavelet transform. Compression is achieved by using SVM regression to approximate wavelet coefficients. Based on the characteristic of wavelet decomposition, the coefficient correlation in wavelet domain is analyzed. According to the correlation characteristic at different scales and orientations, three kinds of arranging methods of wavelet coefficients are designed, which make SVM compress the coefficients more efficiently. Moreover, an effective entropy coder based on run-length and arithmetic coding is used to encode the support vectors and weights. Experimental results show that the compression performance of the algorithm achieve much improvement. [ABSTRACT FROM AUTHOR]

Published
2005
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