Correlation-Based Approach to Color Image Compression

Evgeny Gershikov, Emilia Lavi (Burlak) and Moshe Porat,
Department of Electrical Engineering, 
Technion - Israel Institute of Technology 
Haifa 32000, Israel


Abstract

Most coding techniques for color image compression employ a 
de-correlation approach - the RGB primaries are transformed 
into a de-correlated color space, such as YUV or YCbCr, then 
the de-correlated color components are encoded separately.  
Examples of this approach are the JPEG and JPEG2000 image 
compression standards. A different method, of a Correlation 
Based Approach (CBA) is presented in this paper. Instead of 
de-correlating the color primaries, we employ the existing 
inter-color correlation to approximate two of the components 
as a parametric function of the third one, called the base 
component. We then propose to encode the parameters of the 
approximation function and part of the approximation errors. 
We use the DCT (Discrete Cosine Transform) block transform to 
enhance the algorithms performance. Thus the approximation of 
two of the color components based on the third color is 
performed for each DCT subband separately.  We use the 
Rate-Distortion theory of subband transform coders to optimize 
the algorithms bits allocation for each subband and to find 
the optimal color components transform to be applied prior 
to coding. This pre-processing stage is similar to the use of 
the RGB to YUV transform in JPEG and may further enhance the 
algorithms performance.  We introduce and compare two versions 
of the new algorithm and show that by using a Laplacian 
probability model for the DCT coefficients as well as down-sampling 
the subordinate colors, the compression results are further 
improved.  Simulation results are provided showing that the new 
CBA algorithms are superior to presently available algorithms 
based on the common de-correlation approach, such as JPEG.





Elsevier Signal Processing: Image Communication,
Volume 22, Issue 9, pp. 719-733 (2007).