COLOR IMAGE CODING USING REGIONAL CORRELATION
OF PRIMARY COLORS
Yalon Roterman and Moshe Porat,
Department of Electrical Engineering,
Technion - Israel Institute of Technology
Haifa 32000, Israel
Most color compression systems reduce the redundancies between the
RGB color components by transforming the color primaries into a
decorrelated color space, such as YIQ or YUV. In this paper a different
compression approach is proposed. Since the high correlation of the
RGB color channels implicitly suggests a localized functional relation
between the components, it is used here in an alternative framework,
by approximating subordinate colors as functions of a base color
allowing that only a reduced number of parameters is required for
coding the color information. Furthermore, since this correlation is
particularly high locally, the image is first sub-divided into regions
and for each region the correlation is analyzed and exploited separately.
The size of the encoded regions is gradually reduced to allow
progressively a more refined description of the transmitted image.
Compression results of this progressive approach, which could be useful
for slower communication channels, are presented and compared with
JPEG as a typical example of the decorrelation approach. Our
conclusion is that the proposed new approach to progressive image
coding could be superior to presently available compression techniques.
Elsevier Image and Vision Computing, Vol. 25, pp. 637-651 (2007).
Moshe Porat's Homepage (http://vision.technion.ac.il/mp)