Adaptive prediction using local area based predictor evaluation

Adaptive prediction is required to account for the nonstationarity in natural images. An efficient approach to adaptive prediction is presented which uses a local causal area to evaluate a number of fixed sub-predictors. Various schemes are proposed to utilise this information, including a rank-order based approach, two stage adaptive selection utilising median filtering, an adaptive combination method and a technique incorporating adaptive selection followed by adaptive combination which, coupled with prediction error feedback and adaptive arithmetic coding, produces results slightly superior to CALIC