Minimum entropy transform using Gabor wavelets for image compression

Most image compression methods are based on the use of the DCT or (bi-)orthogonal wavelets. However, in many cases improved performance in terms of visual quality can be expected if we consider a human visual system based model. The aim of this paper is to explore the potential of image compression techniques based on the use of nonorthogonal filters such as Gabor wavelets. The compression scheme is performed by a linear wavelet transform with filters similar to 2D Gabor functions through a quantizer based on measurements of the contrast sensitivity function of the human visual system (HVS). The compression performance is evaluated by entropy and error measures. Because of the non-orthogonality property, different image decompositions will have the same reconstruction. Thus, between all possible decompositions, one can be interested specifically in a minimum entropy wavelet transform that minimizes the information redundancy. This process can be considered as a nonlinear Gabor-wavelet transform that can be employed for compression applications. The overall optimization procedure has been implemented as an iterative algorithm producing a significant reduction in the information redundancy.