A Fusion-Based Approach for Blind Contrast-Enhanced Image Ranking

Cameras are now available at extremely low prices due to ongoing advancements in image acquisition hardware. However, the quality of images can be compromised by various distortions that occur throughout the entire process, from acquisition to processing and delivery. Over the past few decades, researchers have primarily focused on developing algorithms to assess the quality of distorted images. Unfortunately, certain distortions can also result from enhancement processes, such as over-enhancement and color saturation. Although there are metrics available for measuring contrast levels in images, there is currently no standard metric for evaluating the extent and effects of contrast enhancement. In this paper, we propose a new framework that expands the evaluation of contrast levels to ranking contrast-enhanced images. Our technique involves extracting a new set of features that accurately describe the effects of contrast enhancement. Furthermore, we integrate additional statistical indicators, such as skewness and kurtosis, which describe the degree of visual satisfaction linked to human perception. These identified characteristics are subsequently use with a simple classification module to determine the rank order for a given collection of contrast enhanced images. The results show excellent accuracy in correct ranking which outperforms state-of-the-art by more than $15 \%$.