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.

Research on visual quality assessment has been active during the last decade. In this work, we provide an in-depth review of recent developments in the field. As compared with existing survey papers, our current work has several unique contributions. First, besides image quality databases and metrics, we put equal emphasis on video quality databases and metrics as this is a less investigated area. Second, we discuss the application of visual quality evaluation to perceptual coding as an example for applications. Third, we benchmark the performance of state-of-the-art visual quality metrics with experiments. Finally, future trends in visual quality assessment are discussed.

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