A Novel Approach to Image Recoloring for Color Vision Deficiency.

In this paper, a novel method to modify color images for the protanopia and deuteranopia color vision deficiencies is proposed. The method admits certain criteria, such as preserving image naturalness and color contrast enhancement. Four modules are employed in the process. First, fuzzy clustering-based color segmentation extracts key colors (which are the cluster centers) of the input image. Second, the key colors are mapped onto the CIE 1931 chromaticity diagram. Then, using the concept of confusion line (i.e., loci of colors confused by the color-blind), a sophisticated mechanism translates (i.e., removes) key colors lying on the same confusion line to different confusion lines so that they can be discriminated by the color-blind. In the third module, the key colors are further adapted by optimizing a regularized objective function that combines the aforementioned criteria. Fourth, the recolored image is obtained by color transfer that involves the adapted key colors and the associated fuzzy clusters. Three related methods are compared with the proposed one, using two performance indices, and evaluated by several experiments over 195 natural images and six digitized art paintings. The main outcomes of the comparative analysis are as follows. (a) Quantitative evaluation based on nonparametric statistical analysis is conducted by comparing the proposed method to each one of the other three methods for protanopia and deuteranopia, and for each index. In most of the comparisons, the Bonferroni adjusted p-values are <0.015, favoring the superiority of the proposed method. (b) Qualitative evaluation verifies the aesthetic appearance of the recolored images.

On the systematic development of fast fuzzy vector quantization for grayscale image compression.

In this paper we propose a learning mechanism to systematically design fast fuzzy clustering-based vector quantizers. Although the utilization of fuzzy clustering in vector quantization is able to reduce the dependence on initialization, it finally obtains high computational cost. This problem has been investigated by many researchers. So far, the most widely used solution is to equip the quantizer with specialized strategies for the smooth transition from fuzzy to crisp conditions. Hereby, we propose an enhanced solution to that problem. In our contribution we combine three different learning modules. The first one concerns the reduction of the number of codewords that are affected by a specific training pattern. The second one acts to reduce the number of training patterns involved in the design process. The sequential implementation of the above two modules manages to significantly reduce the computational cost of the quantizer. However, the potential risk related to the implementation of the first module is the high probability to generate small and badly delineated clusters. To handle this problem we apply, in the third module, a novel cluster distortion equalization process, according to which the codewords of small clusters are moved to the neighborhood of large ones in order to increase their size and become more competitive, obtaining a better local minimum. The proposed algorithm is rigorously evaluated and compared to other sophisticated methods in terms of grayscale image compression.