Abstract-In this letter, a novel decolorization method is proposed to convert color images into grayscale. The proposed method, called CorrC2G, estimates the three global linear weighting parameters of the color to gray conversion by correlation. These parameters are estimated directly from the correlations between each channel of the RGB image and a contrast image. The proposed method works directly on the RGB channels; it does not use any edge information nor any optimization or training. The objective and subjective experimental results on three available benchmark datasets of color to gray conversion, e.g. Cadik, CSDD and Color250, show that the proposed decolorization method is highly efficient and comparable to recent state-of-the-art decolorization methods. The MATLAB source code of the proposed method is available at:
Applications of perceptual image quality assessment (IQA) in image and video processing, such as image acquisition, image compression, image restoration, and multimedia communication, have led to the development of many IQA metrics. In this paper, a reliable full reference IQA model is proposed that utilize gradient similarity (GS), chromaticity similarity (CS), and deviation pooling (DP). By considering the shortcomings of the commonly used GS to model the human visual system (HVS), a new GS is proposed through a fusion technique that is more likely to follow HVS. We propose an efficient and effective formulation to calculate the joint similarity map of two chromatic channels for the purpose of measuring color changes. In comparison with a commonly used formulation in the literature, the proposed CS map is shown to be more efficient and provide comparable or better quality predictions. Motivated by a recent work that utilizes the standard DP, a general formulation of the DP is presented in this paper and used to compute a final score from the proposed GS and CS maps. This proposed formulation of DP benefits from the Minkowski pooling and a proposed power pooling as well. The experimental results on six data sets of natural images, a synthetic data set, and a digitally retouched dataset show that the proposed index provides comparable or better quality predictions than the most recent and competing state-of-the-art IQA metrics in the literature, it is reliable and has low complexity. The MATLAB source code of the proposed metric is available at https://dl.dropboxusercontent.com/u/74505502/MDSI.m.
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