White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo


Abdul Manap, Redzuan (2018) NEW LEARNING FRAMEWORKS FOR BLIND IMAGE QUALITY ASSESSMENT MODEL. PhD thesis, University of Sheffield.

Final Version July 2018.pdf
Available under License Creative Commons Attribution-Noncommercial-No Derivative Works 2.0 UK: England & Wales.

Download (3403Kb) | Preview


The focus of this thesis is on image quality assessment, specifically for problems of assessing the quality of an image blindly or without reference information. There are significant efforts over the last decade in developing objective blind models that can assess image quality as perceived by humans. Various models have been introduced, achieving highly competitive performances and high in correlation with subjective perceptual measures. However, there are still limitations on these models before they can be viable replacements to traditional image metrics over a wide range of image processing applications. This thesis addresses several limitations. The thesis first proposes a new framework to learn a blind image quality model with minimal training requirements, operates locally and has ability to identify distortion in the assessed image. To increase the model’s performance, the thesis then modifies the framework by considering an aspect of human vision tendency, which is often ignored by previous models. Finally, the thesis presents another framework that enable a model to simultaneously learn quality prediction for images affected by different distortion types.

Item Type: Thesis (PhD)
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Electronic and Electrical Engineering (Sheffield)
Identification Number/EthosID: uk.bl.ethos.749498
Depositing User: Mr Redzuan Abdul Manap
Date Deposited: 16 Aug 2018 08:31
Last Modified: 12 Oct 2018 09:55
URI: http://etheses.whiterose.ac.uk/id/eprint/20945

You do not need to contact us to get a copy of this thesis. Please use the 'Download' link(s) above to get a copy.
You can contact us about this thesis. If you need to make a general enquiry, please see the Contact us page.

Actions (repository staff only: login required)