An efficient and robust approach to adaptive prediction is presented which uses a local causal area to evaluate a number of individual fixed sub-predictors. Various schemes are utilized to exploit the resulting information, including a rank-order based approach, a two stage adaptive selection technique utilizing median filtering, a technique for adaptive combination and a technique incorporating adaptive selection followed by adaptive combination. The respective selection and combination schemes display superior results for particular image types. When the proposed predictors are coupled with prediction error feedback and adaptive arithmetic coding, they produce results superior to CALIC. To produce a more robust predictor an additional stage based on one of the proposed selection schemes is proposed. The result is an adaptive prediction scheme which effectively utilizes the principles of predictor combination and selection. Different forms of the latter stage predictor are explored, which are shown to improve overall predictor performance. A selection based approach is also demonstrated to be more robust than combination based schemes.