Combining Quality Metrics for Improved HDR Image Quality Assessment

摘要

We improve High Dynamic Range (HDR) Image Quality Assessment (IQA) using a full reference approach that combines results from various quality metrics (HDR-CQM). We combine metrics designed for different applications such as HDR, SDR and color difference measures in a single unifying framework and non-linearly combine the scores from different quality metrics using support vector machine regression. To improve performance and reduce complexity, we use the Sequential Forward Selection technique to select a subset of metrics from a list of quality metrics. We evaluate the performance on two publicly available databases with different types of distortion and demonstrate improved performance using HDR-CQM as compared to several existing IQA metrics. We also show the generality and robustness of our approach using cross-database evaluation.