Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509179
Title: Image quality assessment using algorithmic and machine learning techniques
Author: Li, Cui
Awarding Body: University of Aberdeen
Current Institution: University of Aberdeen
Date of Award: 2009
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Abstract:
The first area of work is to assess image quality by measuring the similarity between edge map of a distorted image and that of its original version using classical edge quality evaluation metrics.  Experiments show that comparing edge maps of original and distorted images gives a better result than comparing the images themselves.  Based on redefined source and distortion models, a novel FR image quality assessment metric DQM is proposed, which is proved by subsequent experiments to be competitive with state-of-the-art metrics (SSIM, IFC, VIF, etc.).  The thesis also proposes several image quality metrics based on a framework for developing image quality assessment algorithms with the help of data-driven models (multiple linear regression, artificial neural network and support vector machine). Among them, CAM_BPNN and CAM_SVM perform better than SSIM and can even compete with its improved multi-scale version MSSIM.  Apart from the research about FR image quality assessment, a novel RR image quality assessment system is proposed, based on low-level features (corner, edge and symmetry).
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.509179  DOI: Not available
Keywords: Imaging systems ; Pattern recognition systems ; Image processing ; Artificial intelligence
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