An image compression approach capable of exploiting redundancies in groups of images is introduced. The
approach is based on image segmentation, texture analysis and texture synthesis. The proposed algorithm
extracts textured regions from an image and merges them with similar texture data from other images, in order
to take advantage of textural re-occurrences between the images. The texture extraction is done by taking
overlapping rectangular texture parameter samples from the input image(s), and using a clustering algorithm
to merge them into spatially connected regions, resulting in a polygonal texture map. The textures of that
map are henceforth analysed by extracting various features from the texture regions. Using a metric defined
on these features, the textures are then merged with entries from a central database, which consists of all the
textures in all the images of the image collection, so that for each image, only a polygonal segmentation map
and references into this texture database need to be stored. Decoding (decompression) works by extracting the
polygonal texture map followed by filling the map regions with patterns generated using texture synthesis based
on the texture feature vectors from the database.
This paper proposes to extend the Karhunen-Loeve compression algorithm to multiple images. The resulting
algorithm is compared against single-image Karhunen Loeve as well as algorithms based on the Discrete Cosine
Transformation (DCT).
Futhermore, various methods for obtaining compressable clusters from large image databases are evaluated.
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