A novel color image quantization algorithm is
developed. This new algorithm addresses the
question of how to incorporate the principle of
human visual perception to color variation
sensitivity into color image quantization process.
Color variation measure (CVM) is calculated first
in CIE Lab color space. CVM is used to evaluate
color variation and to coarsely segment the image.
Considering both color variation and homogeneity
of the image, the number of colors that should be
used for each segmented region can be determined.
Finally, CF-tree algorithm is applied to classify
pixels into their corresponding palette colors.
The quantized error of our proposed algorithm is
small due to the combination of human visual
perception and color variation. Experimental
results reveal the superiority of the proposed
approach in solving the color image quantization
Chang, D.J. Lee, Y. Hong, J.K. Archibald
and D. Liang, “ Using Color Variation
Measure to Mimic Human Visual Perception for
Color Image Quantization, ” Journal of
Multimedia, vol. 3/2, p. 20-27, June 2008.
D.J. Lee, and J.K. Archibald, “ Using Color
Variation Measure to Mimic Human Visual
Perception for Color Image Quantization, ”The
First International Journal on
Information Technology and Intelligent
Computing, vol. 2/3, August 2007.
Chang and D.J. Lee, “Color Image
Quantization Using Color Variation Measure,
”The First IEEE Symposium on
Computational Intelligence in Image and Signal
Processing (CIISP 2007), Honolulu, Hawaii,
USA, April 1-5, 2007.
image to view paper poster)