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Title: Exploiting statistical properties of wavelet coefficients for image/video processing and analysis tasks
Author: Al-Jawad, Naseer
Awarding Body: University of Buckingham
Current Institution: University of Buckingham
Date of Award: 2009
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In this thesis the statistical properties of wavelet transform high frequency sub-bands has been used and exploited in three main different applications. These applications are; Image/video feature preserving compression, Face Biometric content based video retrieval and Face feature extraction for face verification and recognition. The main idea of this thesis was also used previously in watermarking (Dietze 2005) where the watermark can be hidden automatically near the significant features in the wavelet sub-bands. The idea is also used in image compression where special integer compression applied on low constrained devices (Ehlers 2008). In image quality measurement, Laplace Distribution Histogram (LDH) also used to measure the image quality. The theoretical LOH of any high frequency wavelet sub-band can match the histogram produced from the same high frequency wavelet sub-band of a high quality picture, where the noisy or blurred one can have a LOH which can be fitted to the theoretical one (Wang and Simoncelli 2005). Some research used the idea of wavelet high frequency sub-band features extraction implicitly, in this thesis we are focussed explicitly on using the statistical properties of the wavelet sub-bands in its multi-resolution wavelet transform. The fact that each high frequency wavelet sub-band frequencies have a Laplace Distribution (LO) (or so called General Gaussian distribution) has been mentioned in the literature. Here the relation between the statistical properties of the wavelet high frequency sub-bands and the feature extractions is well established. LOH has two tails, this make the LOH shape either symmetrical or skewed to the left, or the right This symmetry or skewing is normally around the mean which is theoretically equal to zero. In our study we paid a deep attention for these tails, these tails actually represent the image significant features which can be mapped from the wavelet domain to the spatial domain. The features can be maintained, accessed, and modified very easily using a certain threshold.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available