|Opis:||The scope of this doctoral dissertation is an algorithm for the compression of domain-bound image sequences. The notion of a domain-bound image sequence is used as a description for ordered image sequences of a linked content, which represent either the temporal, or the spatial course of change of an arbitrary domain. The theoretical description translates in practice, within the scope of our algorithm, to two similar tasks: the compression of temporal image sequences, thus videos, and the compression of spatial image sequences, for example medical image sets acquired by CT or MRI technologies.
In this dissertation we describe the structure and functioning of the algorithm, which tackles the mentioned problem by projection to principal component space. Initially we introduce the mathematical background, which is the foundation for principal component analysis, a method frequently used in statistics. It is this method, which is the origin for the computation of projection spaces that can be used to represent images from a given domain, whereby the type of the image sequence is irrelevant.
In order to extend the domain independence, provided by the underlying mathematical method, to the scope of the compression algorithm, selecting the subsequence of images that are the origin for the projection space computation is the first important step. For this task we use a double-criterion algorithm, which selects the images - we name them base images - based on their mutual deviation and distance within the input sequence. A projection space sequence is computed from the selected sequence of base images using a concept introduced in this dissertation, which defines that adjacent projection spaces are computed from overlapping sets of base images, which have thus at least one element in common. In analogy to the sliding window concept we describe this approach as a ''sliding eigenspace''.
In parallel we introduce a method of computing projection spaces that allows for a later reconstruction of the input data at a significantly reduced computational cost. This is achieved by including intermediary computational results into the compressed data representation, while the influence on the compression ratio is insignificant.
In the experimental analysis we provide a comparison between the developed algorithm, the previously generally used method based on projection to principal component space, and the H.264 standard. Thereby we prove that in terms of visual quality the algorithm does not only outperform the previously used method, but is actually capable of competing in it, as well as in compression ratio with H.264. The experimental results are further confirmed by a theoretical analysis, where we formally prove the advantages of the developed algorithm and evaluate the impact of the methods control parameters on compression efficiency. |