Robust HDR image quality assessment using combination of quality metrics

We propose a framework to combine various quality metrics using a full reference approach for High Dynamic Range (HDR) Image quality assessment (IQA). We combine scores from metrics exclusively designed for different applications such as HDR, Standard Dynamic Range (SDR) and color difference measures, in a non-linear manner using machine learning (ML) approaches with weights determined during an offline training process. We explore various ML techniques and find that support vector machine regression and gradient boosting regression trees are effective. To improve performance and reduce complexity, we use the back-tracking based Sequential Floating Forward Selection technique during training to include a subset of metrics from a list of quality metrics in our model. We evaluate the performance on five publicly available calibrated HDR databases with different types of distortion (including different types of compression, Gaussian noise, gamut mismatch, chromatic distortions and so on) and demonstrate improved performance using our method as compared to several existing IQA metrics. We perform extensive statistical analysis to demonstrate significant improvement over existing approaches and show the generality and robustness of our approach using cross-database validation.