Set redundancy, the enhanced compression model, and methods for compressing sets of similar images

摘要: Image compression is the process of reducing number bits required to represent an image. This can be achieved by (or ideally, eliminating) various types redundancy that exist in imaging data. All current image methods are based on same theoretical model, which targets found individual images. However, this model ignores additional type exists sets similar images, "set redundancy". The source set common information existing more than one a Set recognized appearance pixel values at regions, comparable histograms, features, or analogous distributions edges. research explores concept and establishes its importance for compression. A new proposed, Enhanced Compression Model, extends including extraction. requirements restrictions extraction discussed, practical implement it developed. These collectively referred as SRC Redundancy methods. Three presented: Min-Max Differential (MMD) method, Predictive (MMP) Centroid method. According combined with any technique achieve higher ratios when compressing images. One best application areas Model medical imaging. Medical databases usually store large images and, therefore, contain significant amounts redundancy. Tests were performed implementing test database CT MR brain results show average much two-fold improvement performance standard techniques they In addition, developed fast, lossless, easy implement.