Photo enhancement is an important and practical problem in computer vision. In real-word applications, there are massive needs for photo quality enhancement. Image cropping and image color enhancement are two common operations used to improve the visual quality of photographs. By learning from human examples, we propose automatic photo enhancement method which implements these two fundamental operations. / For image cropping, we present an automatic cropping technique that accounts for the two primary considerations of people when they crop: removal of distracting content, and enhancement of overall composition. Our approach utilizes a large training set consisting of photos before and after cropping by expert photographers to learn how to evaluate these two factors in a crop. In contrast to the many methods that exist for general assessment of image quality, ours specifically examines differences between the original and cropped photo in solving for the crop parameters. To this end, several novel image features are proposed to model the changes in image content and composition when a crop is applied. The effectiveness of each feature is empirically analyzed in determining a final feature set for crop computation. Our experiments demonstrate improvements of our method over recent cropping algorithms on a broad range of images. / We also present a machine-learned ranking approach for automatically enhancing the color of a photograph. Unlike previous techniques that train on pairs of images before and after adjustment by a human user, our method takes into account the intermediate steps taken in the enhancement process, which provide detailed information on the person's color preferences. To make use of this data, we formulate the color enhancement task as a learning-to-rank problem in which ordered pairs of images are used for training, and then various color enhancements of a novel input image can be evaluated from their corresponding rank values. From the parallels between the decision tree structures we use for ranking and the decisions made by a human during the editing process, we posit that breaking a full enhancement sequence into individual steps can facilitate training. Our experiments show that this approach compares well to existing methods for automatic color enhancement. / Yan, Jianzhou. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2014. / Includes bibliographical references (leaves 105-115). / Title from PDF title page (viewed on 30, November, 2016).