Thesis (Ph. D.)--University of Hong Kong, 1997. / Includes bibliographical references.

Thesis (M. Phil.)--University of Hong Kong, 1999. / Includes bibliographical references (leaves 105-107).

Thesis (Ph. D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 126-132).

Thesis (Ph.D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 126-132) Also available in print.

Theses (M.S.)--Marshall University, 2006. / Title from document title page. Includes abstract. Document formatted into pages: contains ix, 99 pages. Bibliography: p. 97-99.

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<p>Image compression is widely applied to medical imaging, remote sensing applications, biomedical diagnosis, multimedia applications and so on [1]-[4]. In many cases, considering the factor of image quality, we use a lossless compression method to compress the image.</p> <p>In this thesis work, we propose bit-error aware lossless compression algorithms for color image compression subject to bit-error rate during transmission. Each of our proposed algorithms includes three stages. The first stage is to convert the RGB images to YCrCb images, and the second stage predicts the transformed images to generate the residue sequences. Optimization algorithms are used to search the best combination of the image conversion and prediction. At the last stage, <a>the generated residue sequences are encoded by several residue coding algorithms, which are 2-D and 1-D bi-level block coding, interval Huffman coding and standard Huffman coding algorithms</a>. Key parameters, such as color transformation information, predictor parameters and residue coding parameters, are protected by using (7,4) Hamming code during image transmission, </p> <p><a>The compression ratio (CR) and peak signal to noise ratio (PSNR)</a> are two significant performance indicators which are used to evaluate the experimental results. According to the experimental results, the 2-D bi-level block coding algorithm is verified as the best coding method.</p>

As technology has advanced, data has become more and more important. The more breakthroughs are achieved, the more data is needed to support them. As a result, more storage is required in the system's memory. Compression is therefore required. Before it can be stored, the data must be compressed. To ensure that information is not lost, efficient compression is necessary. This also makes sure that there is no redundancy in the data that is being kept and stored. Compressive sensing has emerged as a new field of compression thanks to developments in sparse optimization. Rather than relying just on compression and sensing formulations, the theory blends the two. The objective of this thesis is to analyze the concept of compressive sensing and to study several reconstruction algorithms. Additionally, a few of the algorithms were put into practice. This thesis also included a model of the ECG, which is vital in determining the health of the heart. For the most part, the ECG is utilized to diagnose heart illness, and a modified synthetic ECG can be used to mimic some of these arrhythmias.

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Images are forming an increasingly large part of modern communications, bringing the need for efficient and effective compression. Many techniques developed for this purpose include transform coding, vector quantization and neural networks. In this thesis, a new neural network method is used to achieve image compression. This work extends the use of 2-layer neural networks to a combination of cascaded networks with one node in the hidden layer. A redistribution of the gray levels in the training phase is implemented in a random fashion to make the minimization of the mean square error applicable to a broad range of images. The computational complexity of this approach is analyzed in terms of overall number of weights and overall convergence. Image quality is measured objectively, using peak signal-to-noise ratio and subjectively, using perception. The effects of different image contents and compression ratios are assessed. Results show the performance superiority of cascaded neural networks compared to that of fixedarchitecture training paradigms especially at high compression ratios. The proposed new method is implemented in MATLAB. The results obtained, such as compression ratio and computing time of the compressed images, are presented.