Image group compression using texture databases

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.