Lagrangian Multiplier Optimization Using Markov Chain Based Rate and Piecewise Approximated Distortion Models

摘要

The traditional Lagrangian RDO algorithm assumes the transformed residues as memo- ryless random variables, and then doesn't perform well when the prediction residues posses the strong temporal correlations. We extend the RDO by modeling the residues as the first-order Markov source and calibrating the distortion model with the piecewise approximation function.