| Summary: | This thesis is dedicated to investigating image watermarking techniques based on the recently proposed transform called 'Shearlet' as the watermark embedding domain. The aim is to obtain new levels of imperceptibility and robustness which lead to higher data hiding capacity. With this idea in mind, new image watermarking algorithms in the Discrete Shearlet Transform domain are developed. First, combined with state of the art spread spectrum embedding methodology, a new watermarking algorithm using DST is designed in order to obtain better performance. The system was tested using five common types of image attacks. The results indicated that a combination of DST and spread spectrum embedding was more rQbust and more imperceptible compared with two well-known watermarking systems based on OCT and DWT domain, using the same embedding strategy. Second, a new perceptual image watermarking scheme using discrete Shearlet transform was developed by adapting a spatial visual model to the structure of the DST decompositions. The system performance was compared, under the same condition using the same embedding and extracting strategy with two watermarking systems based on DWT and DTCWT domain. Experimental results show the proposed method's efficiency by having higher imperceptibility and capacity and, at the same time, being more robust against some of the attacks. Finally, a Shearlet transform-based watermarking framework is proposed for blind watermarking, improving the other transformed-based methods. In order to develop this blind watermarking system, statistical modelling was applied to describe the behaviour of discrete Shearlet transform coefficients based on different sub-bands and resolutions. In order to investigate system performance, the obtained results are compared with watermarking systems based on DWT using the same embedding strategy. The overall results indicate the improvements in performance in order to assess the claims made during this research about the usage of the Discrete Shearlet Transform as a new embedding domain.
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