A learning-based approach to image quality assessment

Research on visual quality assessment has been active during the last decade. This dissertation consists of six parts centered on this subject. In Chapter 1, we highlight the significance and contributions of our research work. The previous work in this area is also thoroughly reviewed.;In Chapter 2, we provide an in-depth review of recent developments in the field. As compared with others' work, our survey has several contributions. First, besides image quality databases and metrics, we put emphasis on video quality databases and metrics since this is a less investigated area. Second, we discuss the application of visual quality evaluation to perceptual coding as an example for applications. Thirdly, we compare the performance of state-of-the-art visual quality metrics with experiments. Finally, we introduce the machine learning methods that can be applied on visual quality assessment.;In Chapter 3, a new methodology for objective image quality assessment (IQA) with multi-method fusion (MMF) is proposed. 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 a 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 3 quality assessment methods are included in the fusion process. The proposed MMF method using support vector regression (SVR) is shown to outperform a large number of existing IQA methods by a significant margin when being tested in six representative databases.;In Chapter 4, an ensemble method for full-reference image quality assessment (IQA) based on the parallel boosting (or ParaBoost in short) idea is proposed in this work. We first extract features from existing image quality metrics and train them to form basic image quality scorers (BIQSs). Then, we select additional features to address specific distortion types and train them to construct auxiliary image quality scorers (AIQSs). Both BIQSs and AIQSs are trained on small image subsets of certain distortion types and, as a result, they are weak performers with respect to a wide variety of distortions. Finally, we adopt the ParaBoost framework to fuse the scores of BIQSs and AIQSs to evaluate images containing a wide range of distortion types. This ParaBoost methodology can be easily extended to images of new distortion types. Extensive experiments are conducted to demonstrate the superior performance of the ParaBoost method, which ourperforms existing IQA methods by a significant margin. Specifically, the Spearman rank order correlation coefficients (SROCCs) of the ParaBoost method with respect to the LIVE, CSIQ, TID2008 and TID2013 image quality databases are 0.98, 0.97, 0.98 and 0.96, respectively.;In Chapter 5, a no-reference learning-based approach to assess image quality is presented in this work. The developed features are extracted from multiple perceptual domains, including brightness, contrast, color, distortion, and texture. The features are then trained to become a model (scorer) which can predict scores. The scorer selection algorithm is utilized to help simplify the proposed system. In the final stage, the ensemble method is used to combine the prediction results from all scorers. Being different from other existing image quality assessment (IQA) methods based on natural scene statistics (NSS) or distortion dependent features, the proposed quality prediction model is robust with respect to more than 24 image distortion types. The extensive experiments on two well-known databases confirm the performance robustness of our proposed model.;Chapter 6 summarizes the work presented in the dissertation. In addition, we have pointed out and discussed several possible directions for future visual signal quality assessment, i.e., PSNR or SSIM-modified metrics, multiple strategy and multi-metric fusion approaches, migration of IQA to VQA, joint audiovisual assessment, perceptual image/video coding, and NR quality assessment, with reasoning based upon our experience and understanding of the related research.