Contrast and content preserving HDMR-based color-to-gray conversion

Abstract

Converting a color image into a grayscale image is a complex problem that is based on preserving color contrast, sharpness, and luminance. In this paper, a novel image decolorization algorithm is proposed using High Dimensional Model Representation (HDMR) with an optimization procedure that retains color content and contrast. In the proposed algorithm, a color image is first decomposed into HDMR components and then the components are categorized depending on whether they are colored or colorless. After that, the image is reconstructed by merging the weighted colored and colorless HDMR components. The weight coefficients are determined by an optimization process. The proposed algorithm both visually and quantitatively compared with state-of-the-art methods in the literature using various performance evaluation metrics. As regards all obtained results, the HDMR based image decolorization algorithm is more potent and has better performance in overall comparison. Most importantly, this algorithm has a flexible structure as it is able to produce various grayscale images for different thresholds of visible color contrast which makes this algorithm superior given that it is the only one that accomplishes this feat in the literature to the best of our knowledge.