Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA.
IEEE Trans Image Process. 2013 May;22(5):1793-807. doi: 10.1109/TIP.2012.2236343. Epub 2012 Dec 24.
A new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is presented in this paper. The research is motivated by the observation that there is no single method that can give the best performance in all situations. To achieve MMF, we adopt a regression approach. The new MMF score is set to be the nonlinear combination of scores from multiple methods with suitable weights obtained by a training process. In order to improve the regression results further, we divide distorted images into three to five groups based on the distortion types and perform regression within each group, which is called "context-dependent MMF" (CD-MMF). One task in CD-MMF is to determine the context automatically, which is achieved by a machine learning approach. To further reduce the complexity of MMF, we perform algorithms to select a small subset from the candidate method set. The result is very good even if only three quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.
本文提出了一种新的基于多方法融合（MMF）的客观图像质量评估（IQA）方法。这项研究的动机是观察到没有一种单一的方法可以在所有情况下都表现出最佳性能。为了实现 MMF，我们采用了回归方法。新的 MMF 得分被设置为多个方法得分的非线性组合，其权重由训练过程获得。为了进一步提高回归结果，我们根据失真类型将失真图像分为三到五个组，并在每个组内进行回归，这称为“上下文相关 MMF”（CD-MMF）。CD-MMF 的一项任务是自动确定上下文，这是通过机器学习方法实现的。为了进一步降低 MMF 的复杂性，我们执行算法从候选方法集中选择一个小的子集。即使在融合过程中只包括三种质量评估方法，结果也非常好。在六个代表性数据库中进行测试时，使用支持向量回归的所提出的 MMF 方法被证明明显优于大量现有的 IQA 方法。
