End-to-End Depth Map Compression Framework Via Rgb-To-Depth Structure Priors Learning

We present a general-purpose framework for the optimization of parametric contrast enhancement algorithms. We first define a regression module for image acceptability, which is based on deep neural features and which is trained on a large dataset of user-expressed preferences. This regression module is then used as the objective function of a Bayesian optimization process, guiding the search for the optimal parameters of a given contrast enhancement algorithm. in our experiments we optimize three different contrast enhancement algorithms of varying levels of complexity. The effectiveness of our optimization framework is experimentally confirmed by evaluating the output of the optimized contrast enhancement algorithms with respect to reference enhanced images.