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Fast vector Quantization of Color Image Coding with Single Codebook based on Orthogonal Polynomials

International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 47 - Number 6
Year of Publication: 2012
Krisshnamoorthy R
Punidha R

Krisshnamoorthy R and Punidha R. Article: Fast vector Quantization of Color Image Coding with Single Codebook based on Orthogonal Polynomials. International Journal of Computer Applications 47(6):19-25, June 2012. Full text available. BibTeX

	author = {Krisshnamoorthy R and Punidha R},
	title = {Article: Fast vector Quantization of Color Image Coding with Single Codebook based on Orthogonal Polynomials},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {47},
	number = {6},
	pages = {19-25},
	month = {June},
	note = {Full text available}


In this paper, a new fast vector quantization encoding technique for transform coding of RGB color images that does not require a color coordinate conversion matrix is proposed. The proposed work directly applies the orthogonal polynomials transformation on the input image and transformed training set with reduced dimension is obtained for the vector quantization and hence the proposed work has reduced computational complexity. In the codebook generation phase of vector quantization encoding, a new transformed binary tree algorithm is proposed to construct a single codebook for all the three color components, utilizing the inter-correlation property of the individual color plane as well as interactions among the color planes with the proposed transformation and so a big saving in codebook construction time is achieved. A new transformed tree structured codeword matching algorithm is proposed in order to further reduce the vector quantization encoding time for finding the closest codeword of an input vector. The experimental results show that the proposed algorithm greatly reduces the encoding time when compared with recent fast vector quantization algorithms.


  • Ahmed Swilem. 2010. A fast vector quantization encoding algorithm based on projection pyramid with hadamard transformation. Image and Vision Comput. , 28, (12), pp. 1637-1644.
  • S. Annadurai, M. Sunderasen. 2009. Wavelet based color image compression relying on subband vector quantization, ICGST-GVIP J. , 9, (1), pp. 9-16.
  • Baskar Ramamoorthy, Allen Gersho. 1986. Classified vector quantization of images, IEEE Trans. on Commun. , 34, (11), pp. 1105-1115.
  • Chaur-Heh Hsieh, Wei-Yang Shao, Ming-Haw Jing. 2000. Image compression based on multi-stage vector quantization, J. of Visual Commun. and Image Represent. , 11, (4), pp. 374-384.
  • Chun-Wei Tsai, Chao-Yang Lee. 2009. A fast VQ codebook generation algorithm via pattern reduction, Pattern Recognition Lett. , 30,(7), pp. 653-660.
  • Gersho. A. , Gray. R. M. . 1994. Vector quantization and signal compression, Kluwer Academic Publishers, 1994.
  • Guobin Shen, Bing Zeng, Ming-L. Liou. 2003. Adaptive vector quantization with codebook updating based on locality and history, IEEE Trans. on Image Process. , 12,(3), pp. 283-295.
  • Giuseppe Campobello, Mirko Mantineo, Giuseppe Patane, Marco Russo. 2005. LBGS: a smart approach for very large sets vector quantization, Signal Process: Image Commun. , 20, (1), pp. 91-114.
  • Hsien-wen Tseng, ChinChu Chang. 2005. A very low bit rate image compression using transformed classified vector quantization, Informatica, 29, pp. 335-341.
  • Jim Z. C. Lai, Yi-Ching Liaw. 2009. A novel encoding algorithm for vector quantization using transformed codebook, Pattern Recognit. Lett. , 42, (11), pp. 3065-3070.
  • Jim. Z. C. Lai, Yi-Ching Liaw, Julie Liu. 2008. A fast VQ codebook generation algorithm using codeword displacement, Pattern Recognit. , 41,(1), pp. 315-319.
  • Matsumoto Hiroki, Kichikawa Fumito, Sasazaki Kazuya, Maeda Junji, Szuki Yukinori. 2010. Image compression using vector quantization with variable block size division, IEEJ Electronics, Information and Syst. , 130, (8), pp. 1431-1439.
  • Nasser M. Nasrabad, Yushu. Feng. 1990. Image compression using address vector quantization, IEEE Trans. on Commun. , 38, (12), pp. 2166-2173.
  • Paul Shelley, Xiaobo Li, Bin Han. 2004. A Hybrid quantization scheme for image compression, Image and Vision Comput. , 22, (3), pp. 203- 213.
  • Robert Li, Jung Kim. 2000. Image compression using fast transformed vector quantization, IEEE Proc. of Appl. Imagery Pattern Recognit. Workshop, pp. 141-145.
  • Tikonov, A. N. Arsenin. 1977. Solutions of Ill-posed Problems, John Wiley and Sons, New York, 1977.
  • Yoseph Linde, Andres Buzo, Robert M. Gray. 1980. An algorithm for vector quantizer Design, IEEE Trans. on Commun. , 28, (1), pp. 84-94.
  • Yu-Chen Hu, Bing-Hwang Su, Chin-Chiang Tsou. 2008. Fast VQ codebook search algorithm for grayscale image coding, Image and Vision Comput. , 26, (5), pp. 657-666.