Vector Quantization Compression of the Still Image by Multilayer Perceptron 


Vol. 3,  No. 2, pp. 390-398, Mar.  1996
10.3745/KIPSTE.1996.3.2.390


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  Abstract

In this paper, a new image compression algorithms using the generality of the multilayer perceptron is proposed. Proposed algorithm classifies image into some classes, and trains them thorough the multilayer perceptron. Multilayer perceptron which trained by the above method can do compression and reconstruction of the nontrained image by the generality. Also, it reduces memory size of the side of receiver and quantization error. For the experiment, we divide Lena image into 16 classes and train them through one multilayer perceptron. The experimental results show that we can get excellent reconstruction images by doing compression and reconstruction for Lena image, Dollar image and Statue image.

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[IEEE Style]

L. S. Chan, C. T. Whan, K. J. Hong, "Vector Quantization Compression of the Still Image by Multilayer Perceptron," The Transactions of the Korea Information Processing Society (1994 ~ 2000), vol. 3, no. 2, pp. 390-398, 1996. DOI: 10.3745/KIPSTE.1996.3.2.390.

[ACM Style]

Lee Sang Chan, Choi Tae Whan, and Kim Jin Hong. 1996. Vector Quantization Compression of the Still Image by Multilayer Perceptron. The Transactions of the Korea Information Processing Society (1994 ~ 2000), 3, 2, (1996), 390-398. DOI: 10.3745/KIPSTE.1996.3.2.390.

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