Modified Vector Quantization Method for Image Compression
A low bit rate still image compression scheme by
compressing the indices of Vector Quantization (VQ) and generating
residual codebook is proposed. The indices of VQ are compressed by
exploiting correlation among image blocks, which reduces the bit per
index. A residual codebook similar to VQ codebook is generated that
represents the distortion produced in VQ. Using this residual
codebook the distortion in the reconstructed image is removed,
thereby increasing the image quality. Our scheme combines these two
methods. Experimental results on standard image Lena show that our
scheme can give a reconstructed image with a PSNR value of 31.6 db
at 0.396 bits per pixel. Our scheme is also faster than the existing VQ
variants.
[1] R. M. Gray, "Vector quantization," IEEE Acoustics, speech and Signal
Processing Magazine, pp. 4-29, 1984.
[2] M. Goldberg, P. R. Boucher and S. Shlien, "Image Compression using
adaptive vector quantization," IEEE Transactions on Communication,
Vol. 34, No. 2, pp. 180-187, 1986.
[3] Y. Linde, A. Buzo and R. M. Gray, "An algorithm for vector quantizer
design," IEEE Transactions on Communication, Vol. 28, No. 1, 1980,
pp. 84 - 95.
[4] T.Kim, "Side match and overlap match vector quantizers for images,"
IEEE Trans. Image. Process., vol.28 (1), pp.84-95, 1980.
[5] Z.M.Lu, J.S Pan and S.H Sun, "Image Coding Based on classified sidematch
vector quantization," IEICE Trans.Inf.&Sys., vol.E83-D(12),
pp.2189-2192, Dec. 2000.
[6] Z.M.Lu, B.Yang and S.H Sun, "Image Compression Algorithms based
on side-match vector quantizer with Gradient-Based classifiers," IEICE
Trans.Inf.&Sys., vol.E85-D(9), pp.1414- 1420, September. 2002.
[7] Chia-Hung Yeh, "Jigsaw-puzzle vector quantization for image
compression" , Opt.Eng Vol.43, No.2, pp. 363-370, Feb-2004.
[8] C.H.Hsieh, and J.C Tsai, "Lossless compression of VQ index with
search order Coding," IEEE Trans. Image Processing, Vol.5, No. 11,
pp. 1579- 1582, Nov. 1996.
[9] Chun-Yang Ho, Chaur-Heh Hsieh and Chung-Woei Chao, "Modified
Search Order Coding for Vector Quantization Indexes," Tamkang
Journal of Science and Engineering, Vol.2, No.3, pp. 143- 148, 1999.
[1] R. M. Gray, "Vector quantization," IEEE Acoustics, speech and Signal
Processing Magazine, pp. 4-29, 1984.
[2] M. Goldberg, P. R. Boucher and S. Shlien, "Image Compression using
adaptive vector quantization," IEEE Transactions on Communication,
Vol. 34, No. 2, pp. 180-187, 1986.
[3] Y. Linde, A. Buzo and R. M. Gray, "An algorithm for vector quantizer
design," IEEE Transactions on Communication, Vol. 28, No. 1, 1980,
pp. 84 - 95.
[4] T.Kim, "Side match and overlap match vector quantizers for images,"
IEEE Trans. Image. Process., vol.28 (1), pp.84-95, 1980.
[5] Z.M.Lu, J.S Pan and S.H Sun, "Image Coding Based on classified sidematch
vector quantization," IEICE Trans.Inf.&Sys., vol.E83-D(12),
pp.2189-2192, Dec. 2000.
[6] Z.M.Lu, B.Yang and S.H Sun, "Image Compression Algorithms based
on side-match vector quantizer with Gradient-Based classifiers," IEICE
Trans.Inf.&Sys., vol.E85-D(9), pp.1414- 1420, September. 2002.
[7] Chia-Hung Yeh, "Jigsaw-puzzle vector quantization for image
compression" , Opt.Eng Vol.43, No.2, pp. 363-370, Feb-2004.
[8] C.H.Hsieh, and J.C Tsai, "Lossless compression of VQ index with
search order Coding," IEEE Trans. Image Processing, Vol.5, No. 11,
pp. 1579- 1582, Nov. 1996.
[9] Chun-Yang Ho, Chaur-Heh Hsieh and Chung-Woei Chao, "Modified
Search Order Coding for Vector Quantization Indexes," Tamkang
Journal of Science and Engineering, Vol.2, No.3, pp. 143- 148, 1999.
@article{"International Journal of Information, Control and Computer Sciences:58484", author = "K.Somasundaram and S.Domnic", title = "Modified Vector Quantization Method for Image Compression", abstract = "A low bit rate still image compression scheme by
compressing the indices of Vector Quantization (VQ) and generating
residual codebook is proposed. The indices of VQ are compressed by
exploiting correlation among image blocks, which reduces the bit per
index. A residual codebook similar to VQ codebook is generated that
represents the distortion produced in VQ. Using this residual
codebook the distortion in the reconstructed image is removed,
thereby increasing the image quality. Our scheme combines these two
methods. Experimental results on standard image Lena show that our
scheme can give a reconstructed image with a PSNR value of 31.6 db
at 0.396 bits per pixel. Our scheme is also faster than the existing VQ
variants.", keywords = "Image compression, Vector Quantization, Residual
Codebook.", volume = "2", number = "7", pages = "2474-6", }