A Color Contrast Definition for Perceptually Based Color Image Coding
J. Gutierrez, G. Camps-Valls, M.J. Luque and J. Malo
Affiliation: Department of d'Informatica, Universitat de Valencia, 46100 Burjassot, Valencia, Spain.
The non-linear nature of the human visual response to achromatic contrast is a key element to improve the performance in achromatic image coding. Expressing transform coefficients in the appropriate contrast units is relevant when some particular non-linear processing hasto be applied. In the achromatic case, the use of non-linear psychophysical models is straightforward since achromatic contrast computation from image transform coefficients is quite simple. However, using equivalent color masking models in transform coding is not easy since psychophysical results are expressed in color contrast units which are non-trivially related to the transform coefficients in opponent color spaces.
In this patent we describe a general procedure to define color contrast for any spatial basis functions (such as block-DCT or wavelets) with any chromatic modulation. The proposed definition is based on (1) simple psychophysics to define purely chromatic basis functions, and (2) statistical analysis of the chromatic content of natural images to define the maximum chromatic modulation. The proposed color contrast definition allows for a straightforward extension of the well known non-linear achromatic masking models to the chromatic case for color image coding. In this work, the use of the proposed color contrast definition is illustrated by a particular non-linear color image coding scheme based on block- DCT, non-linear perceptual response transforms in YUV color channels, and non-linear machine learning response selection. This non-linear scheme is compared to the equivalent linear (JPEG-like) scheme, where color contrast definition is not relevant due to its linear nature.
Keywords: Color contrast, chromatic gratings, chromatic divisive normalization, constant insensitivity, DCT, image coding, JPEG, support vector machine (SVM), support vector regression, Divisive normalization, supra-threshold brightness, radial Gaussianization, colour image database, Digital image processing, Computation
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