PEER GROUP FILTERING AND PERCEPTUAL COLOR IMAGE
QUANTIZATION

Yining Deng, Charles Kenny, Michael S. Moore and B.S.Manjunath
Department of Electricalk and Computer Engineering
University of California, Santa Barbara, CA 93106-9360
{deng, msmoorc, kenny, manj}@iplab.ece.ucsb.edu

Abstract

In the first part of this work, peer group filtering(PGF), a
nonlinear algorithm for image smoothing and impulse noise
removal in color images is presented. The algorithm replaces
each image pixel with the weighted average of its peer group
members, which are classified based on the color similarity of the
neighboring pixels. Results show that it effectively removes the
noise and smoothes the color images without blurring edges and
details. In the second part of the work, PGF is used as a preprocessing
 step for color quantization. Local statistics obtained after
PGF are used as weights in the quantization to suppress color
clusters in detailed regions, since human perception is less sensitive
to the difference in these areas. As a result, very coarse
quantization can be obtained while preserving the color information
in the original images. This can be useful in color image segmentation
and color image retrieval applications.