A color space plays an important role in color image processing and color vision applications. While compressing images/videos, properties of the human visual system are used to remove image details unperceivable by the human eye, appropriately called psychovisual redundancies. This is where the effect of the color spaces' properties on compression efficiency is introduced. In this work, we study the suitability of various color spaces for compression of images and videos. This review work is undertaken in two stages. Initially, a comprehensive review of the published color spaces is done. These color spaces are classified and their advantages, limitations, and applications are also highlighted. Next, the color spaces are quantitatively analyzed and benchmarked in the perspective of image and video compression algorithms, to identify and evaluate crucial color space parameters for image and video compression algorithms.

Color space dimensionality possesses main problem in fast processing of color images so appropriate sampling of color images is very important. Unlike the existing statistical sampling algorithm, in this paper, a biologically inspired non-linear color image sampling technique has been proposed using non-uniform quantization of RGB space. Response of human retinal receptors to various light intensities is non-linear in nature. Buschbaum has qualitatively presented the non-linear tan-sigmoid model of the human vision as against the logarithmic and power law models. An experiment has been carried out on certified normal color vision observers in broad day light conditions to model their color vision. Readings of this experiment were used to compute the parameters of Red, Green and Blue color vision non-linearity presented by Buchsbaum. These parametric non-linearity equations were used to sample the color images and other applications of the work have been proposed. The non-linearity equations with respective parameters represent the models of Red, Green and Blue color vision receptors. Physiological limitations and facts of human vision have been utilized to compute the parameter.

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