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A new automatic compression scheme that adapts to any image set is presented in this thesis. The proposed scheme requires no a priori knowledge on the properties of the image set. This scheme is obtained using a unified graph-theoretical framework that allows for compression strategies to be compared both theoretically and experimentally. This strategy(More)
While the compression of individual images have been studied extensively, there have been fewer studies on the problem of compressing image sets. A number of schemes have been proposed to compress an image set by taking advantage of the inter-image redundancy between pairs of images. In this paper, we present a unified graph-theoretic framework that(More)
Summary form only given. This paper presents a framework to effectively compress sets of images in a lossless manner. An image set is represented as a graph and its minimum spanning tree is computed to decide which images and differences to encode. The Centroid scheme and the previous MST scheme can both be represented as a spanning tree in our graph. Thus,(More)
Maintainability is a desired property of software, and a variety of metrics have been proposed for measuring it, focusing on different notions of complexity and code readability. Many practices have been proposed to improve maintainability through code refactorings: improving the cohesion, simplification of interfaces, renamings to improve(More)
A number of minimum spanning tree algorithms have been proposed for lossy compression of image sets. In these algorithms, a complete graph is constructed from the entire image set and possibly an average image, and a minimum spanning tree is used to determine which difference images to encode. In this paper, we propose a hierarchical minimum spanning tree(More)
The automatic compression strategy proposed by Gergel et al. is a near-optimal lossy compression scheme for a given collection of images whose inter-image relationships are unknown. Their algorithm uses the root mean square error (RMSE) as a measure of the similarity between two images, in order to predict the compressibility of the difference image. Gergel(More)
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