2 research outputs found
Blind image quality assessment: from heuristic-based to learning-based
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