General Framework of Image Quality Assessment



The study upon objective image quality assessment (IQA) has entered the era of solid theoretical fundaments and rigorous experimental flows. Although there are extensive IQA methods been proposed, they generally follow similar frameworks in design. The main differences between frameworks are according to how much the implement of a specific method is dependent upon reference images, in which way three classes, full-reference (FR), reduced-reference (RR), and no-reference (NR), are defined. For methods of all the three categories, evitable processing contains quality-aware feature extraction, feature quantification, quality index mapping, and statistical performance evaluation, and the related fields include image processing, statistics, machine learning at the very least. In this chapter, we attempt to introduce the general frameworks that modern IQA methods adopt, explain the specific flow of the methods step-by-step, during which major knowledge about the design and evaluation of the methods would be concerned.


Full-reference Reduced-reference No-reference Feature extraction Statistical evaluation Machine learning 


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Copyright information

© Zhejiang University Press, Hangzhou and Springer-Verlag GmbH Germany 2018

Authors and Affiliations

  1. 1.Zhejiang UniversityHangzhouChina

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