Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xii, 146 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 134-146).

Multi-view three-dimensional television requires many views, which may be synthesized from two-dimensional images with accompanying pixel-wise depth information. This depth image, which typically consists of smooth areas and sharp transitions at object borders, must be consistent with the acquired scene in order for synthesized views to be of good quality. We have previously proposed a depth image coding scheme that preserves significant edges and encodes smooth areas between these. An objective evaluation considering the structural similarity (SSIM) index for synthesized views demonstrated an advantage to the proposed scheme over the High Efficiency Video Coding (HEVC) intra mode in certain cases. However, there were some discrepancies between the outcomes from the objective evaluation and from our visual inspection, which motivated this study of subjective tests. The test was conducted according to ITU-R BT.500-13 recommendation with Stimulus-comparison methods. The results from the subjective test showed that the proposed scheme performs slightly better than HEVC with statistical significance at majority of the tested bit rates for the given contents.

There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform.

Fractal image compression is based on the representation of an image by contractive transforms whose xed points are close to the original image. In the encoding of fractal image compression, most of the time is spent on nding the close match between a range block and a large pool of domain blocks. In this thesis, we use the intra-block variances distributions of domain blocks to reduce the searching space. For nding a close match we need only search the domain blocks whose maximal intra-block variance quadrants are the same as that of the range block in nding a close match. The experiment results show that our algorithm can reduce much encoding time with only slight loss of quality.

Thesis (M.S.)--Naval Postgraduate School, 2002. / Thesis advisor(s): Murali Tummala, Robert Ives. Includes bibliographical references (p. 95-97). Also available online.

Thesis (Ph. D.)--Ohio State University, 2003. / Title from first page of PDF file. Document formatted into pages; contains xxv, 142 p.; also includes graphics. Includes abstract and vita. Advisor: Yuan F. Zheng, Dept. of Electrical and Computer Engineering. Includes bibliographical references (p. 135-142).

Thesis (Ph. D.)--University of Washington, 2003. / Vita. Includes bibliographical references (leaves 94-103).

Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references (leaves 155-176). Available also in a digital version from Dissertation Abstracts.

Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003. / Includes bibliographical references (leaves 63-65). Also available in electronic version. Access restricted to campus users.

The problem of archiving photos is becoming increasingly important as image databases are growing more popular, and larger in size. One could take the example of any social networking website, where users share hundreds of photos, resulting in billions of total images to be stored. Ideally, one would like to use minimal storage to archive these images, by making use of the redundancy that they share, while not sacrificing quality. We suggest a compression algorithm that aims at compressing across images, rather than compressing images individually. This is a very novel approach that has never been adopted before. This report presents the design of a new image database compression tool. In addition to that, we implement a complete system on C++, and show the significant gains that we achieve in some cases, where we compress 90% of the initial data. One of the main tools we use is Locally Sensitive Hashing (LSH), a relatively new technique mainly used for similarity search in high-dimensions. / text