Example-based brightness and contrast enhancement

Brightness and contrast heavily influence image visual quality; therefore, modern digital camera image processing pipelines typically include a brightness and contrast enhancement (BCE) algorithm that enhances visual quality by applying tone mapping to the image. There are many BCE methods published in the literature that are variations of histogram equalization (HE) and contrast stretching (CS). When tested on large image databases, there are always certain images where these algorithms fail because image content is very diverse and a fixed method fails to adapt to this large variation. Our paper addresses this problem. We have developed an example-based BCE algorithm that can adapt its behavior to different scene types by using training examples that are hand-tuned by human observers for optimal visual quality. Our algorithm models the optimal enhancement function from these training images using Principal Component Analysis (PCA). Then, given a new image, the algorithm predicts the best amount of enhancement by extrapolating from closest training images. We have performed perceptual evaluations that conclude that our algorithm effectively enhances brightness and contrast judged by human observers.