Wavelet Transform and Three-Dimensional Data Compression

Abstract : The common framework of this thesis is the three-dimensional (3D) transform approach to the compression of visual data, as video sequences and multispectral (MS) images. Moreover, SAR images compression and lowcomplexity video coding are considered. In particular, the work focuses on 3D wavelet transform (WT), and its variations, such as motion-compensated WT or shape-adaptive WT. This approach can appear natural, as both video sequences and MS images are three-dimensional data. Nevertheless, in the video compression field, 3D-transform approaches have just begun to be competitive with hybrid schemes based on discrete cosine transform (DCT), while, as far as MS images are concerned, the scientific literature misses a comprehensive approach to the compression problem. The 3D WT approach investigated in this thesis has drawn a huge attention by researchers in the data compression field because they hoped it could reply the excellent performances its two-dimensional version achieved in still image coding. Moreover, the WT approach provides a full support for scalability, which seems to be one of the most important topics in the field of multimedia delivery research. A scalable representation of some information is made up of several subsets of data, each of which is an efficient representation of the original information. By taking all the subsets, one has the “maximum quality” version of the original data. By taking only some subsets, one can adjust several reproduction parameters (i.e. reduce resolution or quality) and save the rate corresponding to discarded layers. Such an approach is mandatory for efficient multimedia delivery on heterogeneous networks.
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Human-Computer Interaction. Université Nice Sophia Antipolis, 2005. English


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Submitted on : Tuesday, July 27, 2010 - 11:44:11 AM
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Marco Cagnazzo. Wavelet Transform and Three-Dimensional Data Compression. Human-Computer Interaction. Université Nice Sophia Antipolis, 2005. English. <tel-00506115>

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