Adaptive combination of linear predictors for lossless image compression

Lossless image coding is an essential requirement for medical imaging applications. Lossless image compression techniques usually have two major components: adaptive prediction and adaptive entropy coding. The paper is concerned with adaptive prediction. Recently, several researchers have studied prediction schemes in which the final prediction is formed by a combination of a group of subpredictors. The authors present an overview of this new type of prediction technique. They show that the basic principle of adaptive predictor combination has been extensively studied and applied to many science and engineering problems. They then describe their own combination scheme, which is based on the estimation of the local prediction error variance. Experimental results show that the compression performance of the algorithms that employ this new type of predictor is consistently better than that of state-of-the-art algorithms.