A fuzzy algorithm for color quantization of images

In this paper, we review a number of techniques for fuzzy color quantization. We show that the fuzzy membership paradigm is particularly suited to color quantization, where color cluster boundaries are not well defined. We propose a new fuzzy color quantization technique which incorporates a term for partition index. This algorithm produces better results than fuzzy C-means at a reduced computational cost. We test the results of the fuzzy algorithms using quality metrics which model the perception of the human visual system and illustrate that substantial quality improvements are achieved.

[1]  Jan P. Allebach,et al.  FM screen design using DBS algorithm , 1996, Proceedings of 3rd IEEE International Conference on Image Processing.

[2]  Michael T. Orchard,et al.  Color quantization of images , 1991, IEEE Trans. Signal Process..

[3]  Anil K. Jain,et al.  A Clustering Performance Measure Based on Fuzzy Set Decomposition , 1981, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  James C. Bezdek,et al.  Validity-guided (re)clustering with applications to image segmentation , 1996, IEEE Trans. Fuzzy Syst..

[5]  Rodney L. Miller,et al.  Design of minimum visual modulation halftone patterns , 1991, IEEE Trans. Syst. Man Cybern..

[6]  Lale Akarun,et al.  Fuzzy error diffusion , 2000, IEEE Trans. Image Process..

[7]  Lale Akarun,et al.  Fuzzy algorithms for combined quantization and dithering , 2001, IEEE Trans. Image Process..

[8]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[9]  M. P. Windham Cluster validity for fuzzy clustering algorithms , 1981 .

[10]  R. E. Miller,et al.  Image halftoning using a visual model in error diffusion , 1993 .

[11]  Paul S. Heckbert Color image quantization for frame buffer display , 1982, SIGGRAPH.

[12]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.