Blind image quality assessment: from heuristic-based to learning-based

Cai, Hao (2022) Blind image quality assessment: from heuristic-based to learning-based. Doctoral (PhD) thesis, Memorial University of Newfoundland.

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Image quality assessment (IQA) plays an important role in numerous digital image processing applications, including image compression, image transmission, and image restoration, etc. The goal of objective IQA is to develop computational models that can predict image quality in a way being consistent with human perception. Compared with subjective quality evaluations such as psycho-visual tests, objective IQA metrics have the advantages of predicting image quality automatically and effectively in a timely manner. This thesis focuses on a particular type of objective IQA – blind IQA (BIQA), where the developed methods not only achieve objective IQA, but also are able to assess the perceptual quality of digital images without access to their pristine reference counterparts. Firstly, a novel blind image sharpness evaluator is introduced in Chapter 3, which leverages the discrepancy measures of structural degradation. Secondly, a “completely blind” quality assessment metric for gamut-mapped images is designed in Chapter 4, which does not need subjective quality scores during the model training. Thirdly, a general-purpose BIQA method is presented in Chapter 5, which can evaluate the quality of digital images without prior knowledge on the types of distortions. Finally, in Chapter 6, a deep neural network-based general-purpose BIQA method is proposed, which is fully data driven and trained in an end-to-end manner. In summary, four BIQA methods are introduced in this thesis, where the first three are heuristic-based and the last one is learning-based. Unlike heuristics-based ones, the learning-based method does not involves manually engineered feature designs.

Item Type: Thesis (Doctoral (PhD))
Item ID: 15740
Additional Information: Includes bibliographical references (pages 125-148)
Keywords: perceptual quality, heuristic-based, deep neural networks
Department(s): Science, Faculty of > Computer Science
Date: July 2022
Date Type: Submission
Digital Object Identifier (DOI):
Library of Congress Subject Heading: Neural networks (Computer science); Image processing--Digital techniques; Imaging systems--Image quality

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