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.

我们提出了一个框架，该框架使用高动态范围（HDR）图像质量评估（IQA）的完整参考方法来组合各种质量指标。我们使用机器学习（ML）方法以非线性方式结合专门为不同应用（例如HDR，标准动态范围（SDR）和色差测量）设计的指标的分数，并在离线培训过程中确定权重。我们探索了各种机器学习技术，并发现支持向量机回归树和梯度提升回归树是有效的。为了提高性能并降低复杂性，我们在训练期间使用了基于回溯的顺序浮动正向选择技术，以将来自模型中质量指标列表的指标子集包括在内。我们评估了五个具有不同类型的失真（包括不同类型的压缩，高斯噪声，色域失配，色差等）的公开校准的HDR数据库的性能，并证明了与几种现有IQA指标相比，使用我们的方法可以提高性能。我们进行广泛的统计分析，以证明对现有方法的显着改进，并使用跨数据库验证来证明我们的方法的一般性和鲁棒性。