A perceptual based contrast enhancement metric using AdaBoost
This paper is an attempt to explore a human element not easily solved in the image processing communities. The problem statement is vague but important to address. What is a good image? More specifically, if a low contrast image is presented, at what level of enhancement is good enough for a human observer? This of course depends on diverse elements, e.g., personal preference, emotional state, physical impairments, purpose (object recognition), etc. In this paper we present a new contrast enhancement metric (CEM) that is trained using several simple contrast measures and mean opinion scores obtained from human observations. Our goal is to train the algorithm to mimic a human when selecting an image with the best contrast between two images. For example, the algorithm will accept two images of the same scene with differing (unknown) contrast and will choose which of the two images is ‘better’ according to what a human believes is ‘better’.