Abstract
Today?s web-based automatic image enhancement algorithms decide to apply an enhancement operation by searching for ?similar? images in an online database of images and then applying the same level of enhancement as the image in the database. Two key bottlenecks in these systems are the storage cost for images and the cost of the search. Based on the principles of computational aesthetics, we consider storing task-relevant aesthetic summaries, a set of features which are sufficient to predict the level at which an image enhancement operation should be performed, instead of the entire image. The empirical question, then, is to ensure that the reduced representation indeed maintains enough information so that the resulting operation is perceived to be aesthetically pleasing to humans. We focus on the contrast adjustment operation, an important image enhancement primitive. We empirically study the efficacy of storing a pixelated summary of the 16 most representative colors of an image and performing contrast adjustments on this representation. We tested two variants of the pixelated image: a ?mid-level pixelized version? that retained spatial relationships and allowed for region segmentation and grouping as in the original image and a ?low-level pixelized-random version? which only retained the colors by randomly shuffling the 50 x 50 pixels. In an empirical study on 25 human subjects, we demonstrate that the preferred contrast for the low-level pixelized-random image is comparable to the original image even though it retains very few bits and no semantic information, thereby making it ideal for image matching and retrieval for automated contrast editing. In addition, we use an eye tracking study to show that users focus only on a small central portion of the low-level image, thus improving the performance of image search over commonly used computer vision algorithms to determine interesting key points.
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