A new perspective on decolorization: feature-preserving decolorization

Abstract

Information loss is a major problem in decolorization for color images. For example, the color-to-gray conversion may result in degradation in the contrast that affects visual perception quality. Preserving the source of information as much as possible is the main goal of the decolorization. This paper introduces an efficient contrast preservation method for decolorization based on spatial statistical distributions of the pixel-pair values. The statistical distributions are represented by the co-occurrence matrix in a compact form. Then, a feature extraction step is carried out by making use of this matrix. The feature extraction process is carried out for each channel of the color image and grayscale image to obtain source and target features. Feature-preserving criterion is constructed by $$l_2$$ norm-based quality metric between source feature and target feature. The proposed method is remarkable because it is adaptable to any feature preserving, such as contrast. Moreover, there are no optimization phase, color space conversion, high complexity, and local mapping in the proposed method. Experimental results show that the performance of the proposed method is comparable to the existing decolorization approaches.