Variable-rate colour image quantization based on quadtree segmentation

• Autorzy: Hu Y. C. , Li C. Y. , Chuang J. C. , Lo C. C.
• Języki publikacji: EN

Abstrakty

EN

A novel variable-sized block encoding with threshold control for colour image quantization (CIQ) is presented in this paper. In CIQ, the colour palette used has a great influence on the reconstructed image quality. Typically, a higher image quality and a larger storage cost are obtained when a larger-sized palette is used in CIQ. To cut down the storage cost while preserving quality of the reconstructed images, the threshold control policy for quadtree segmentation is used in this paper. Experimental results show that the proposed method adaptively provides desired bit rates while having better image qualities comparing to CIQ with the usage of multiple palettes of different sizes.

Słowa kluczowe

EN

image compression; colour image quantization; palette design; colour palette

• Wydawca: Stowarzyszenie Elektryków Polskich ; Polska Akademia Nauk ; Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
• Czasopismo: Opto-Electronics Review
• Rocznik: 2011
• Tom: Vol. 19, No. 3
• Strony: 282--289
• Opis fizyczny: Bibliogr. 18 poz., il., wykr.

Twórcy

autor

Hu Y. C.

autor

Li C. Y.

autor

Chuang J. C.

autor

Lo C. C.

• Department of Computer Science and Information Management, Providence University, Taichung, Taiwan, 433 R.O.C.,

Bibliografia

• [1] P. Heckbert, “Colour image quantization for frame buffer display”, Comp. Graph. 16, 297-307 (1982).
• [2] T. Michael and A. Charles, “Colour quantization of images”, IEEE T. Signal Process. 39, 2677-2690 (1991).
• [3] X. Wu and I.H. Witten, “A fast K-means type clustering algorithm”, Technique Report, Department of Computer Science, University of Calgary, Calgary, 1995.
• [4] S.J. Wan, P. Prusinkiewicz, and S.K.M. Wong, “Variance-based colour image quantization for frame buffer display”, Colour Res. Appl. 15, 52-58 (1990).
• [5] C.Y. Yang and J.C. Lin, “RWM-cut for colour image quantization”, Comp. Graph. 20, 577-588 (1996).
• [6] S.C. Cheng and C.K. Yang, “A fast novel technique for colour quantization using reduction of colour space dimensionality”, Pattern Recogn. Lett. 22, 845-856 (2001).
• [7] J.T. Tou and R.C. Gonzalez, Pattern Recognition Principles, Addison-Wesley, Reading, MA, 1974.
• [8] P. Scheunders, “A comparison of clustering algorithms applied to colour image quantization”, Pattern Recogn. Lett. 18, 1379-1384 (1997).
• [9] Y.W. Lim and S.U. Lee, “On the colour image segmentation algorithm based on the thresholding and the fuzzy c-means technique”, Pattern Recogn. 23, 935-952 (1990).
• [10] D. Ozdemir and L. Akarun, “A fuzzy algorithm for colour quantization of images”, Pattern Recogn. 35, 1785-1791 (2002).
• [11] P. Scheunders, “A genetic c-means clustering algorithm applied to colour image quantization”, Pattern Recogn. 30, 859-866 (1997).
• [12] I.S. Hsieh and K.C. Fan, “An adaptive clustering algorithm for colour quantization”, Pattern Recogn. Lett. 21, 337-346 (2000).
• [13] T. Kanungo, D.M. Mount, N.S. Netanyahu, C.D. Piatko, R. Silverman, and A.Y. Wu, “An efficient k-means clustering algorithm: analysis and implementation”, IEEE T. Pattern Anal. 24, 881-892 (2002).
• [14] Y.L. Huang and R.F. Chang, “A fast finite state algorithm for generating RGB palettes of colour quantized images”, J. Inf. Sci. Eng. 20, 771-782 (2004).
• [15] Y.C. Hu and M.G. Li, “A k-means based colour palette design scheme with the use of stable flags”, J. Electron. Imaging 16, art no. 033003 (1-11) (2007).
• [16] Y.C. Hu and B.H. Su, “Accelerated k-means clustering algorithm for colour image quantization”, Imaging Sci. J. 56, 29-40 (2008).
• [17] Y.C. Hu, M.G. Li, and P.Y. Tsai, “Colour palette generation schemes for colour image quantization”, Imaging Sci. J. 57, 46-59 (2009).
• [18] Y.C. Hu and B.H. Su, “Accelerated pixel mapping scheme for colour image quantization”, Imaging Sci. J. 56, 67-78 (2008).