Abstract
Similar images are images with common features, similar pixel distributions, and similar edge distributions. Fields such as medical imaging or satellite imaging often need to store large collections of similar images. In a set of similar images the image similarities represent patterns that consistently appear across all images; this results in “set redundancy”. This paper presents the Centroid method that extracts and uses these similarity patterns to reduce set redundancy and achieve higher lossless compression in sets of similar images. Experimental results with a medical image database demonstrate that the Centroid method can deliver significantly improved image compression.
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