Taxonomy of Performance Measures for Contrast Enhancement

Contrast enhancement is a primitive step in image processing application for enhancing the visual perceptibility which can be treated as the increase in gray level differences between background and foreground with suppression of noises. In literature, various methods have been proposed for contrast enhancement such as bi- histogram and two-dimensional histogram-based methods, etc. Among various methods, the suitable contrast enhancement algorithm should be chosen based on different applications. To find suitability, qualitative and quantitative analysis is also needed. The quantitative analysis can be done with the help of the various performance measures. In this paper, a complete taxonomy of performance measure for contrast enhancement has been discussed and analyzed for the CSIQ database. Among the various performance measures, it is found that Absolute Mean Brightness Error (AMBE), entropy, contrast improvement index, and gradient magnitude similarity deviation (GMSD) are better in terms of the range of operation and the wide variation of low contrast images. It is interpreted that absolute mean brightness requires less time for computation and it is widely used by the researchers for quantitative analysis of contrast enhancement algorithm. This study provides a critical analysis of the several quantitative measures that validate the effectiveness of qualitative measures and motivates to propose an alternate measure that considers the strength of various measures discussed in the literature. These measures quantify the mean brightness shifting, contrast, and entropy preservation.