Adaptive intra prediction algorithm based on extended LARS
Yuichi Sayama, Yukihiro Bandou, Seishi Takamura, Atsushi Shimizu · 2016
It is important to reduce intra prediction error for efficient image coding. However, as the existing methods are based on static structure model, there is no guarantee of their prediction efficiency for images whose structure model are unknown. To remedy the problem of existing methods, we formulate a linear prediction design problem with the goal of minimizing prediction error by placing sparsity constraints on the prediction coefficients. To solve the predictor design problem, we propose a novel method that extends Least Angle Regression(LARS). Coding gain of 1.01% to 2.24% is achieved over HM16.7.