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).
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:509179 |
| Date | January 2009 |
| Creators | Li, Cui |
| Publisher | University of Aberdeen |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=26521 |
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