Reverse Tone Mapping of High Dynamic Range Video Using Gaussian Process Regression

Harshad Kadu, Neeraj J. Gadgil, Guan-Ming Su

Abstract: There is a rapid increase in the amount of High Dynamic Range (HDR) display devices that are able to offer significantly better viewing experience with higher contrast and richer colors. However, there is a considerable shortage of content that is able to offer the much-sought-after HDR experience. One approach is to capture new content using specialized HDR-cameras, which are typically fewer and costlier than the conventional cameras. Another approach is to convert existing Standard Dynamic Range (SDR) content into HDR artificially, such that, it can reasonably mimic the viewing experience that a "true" HDR content would have offered. With the latter approach, we propose a Gaussian Process Regression (GPR) based machine learning method for estimating HDR content from their SDR counterparts. GPR is known as a powerful technique for estimating continuous real-valued functions. Given a set of training SDR-HDR image pairs, our proposed method is able to estimate the reverse tone mapping function that is used to convert SDR signal into its HDR equivalent. Preliminary experimental results indicate that our approach produces visually pleasing HDR images.