Video rate control using conditional mean

This paper presents an efficient rate control algorithm based on estimation theory. We measure a conditional mean by estimating a joint probability density function (PDF) using Parzen's window. The training data can estimate the nonlinear rate-distortion (R-D) relationship between the quantization parameter (QP) and the bits spent for each macroblock depending on the sum of absolute differences (SAD). We increase the accuracy of this joint PDF by clustering the training data depending on the QP values. This localization helps the estimation process to be very accurate. Next we apply a mean operator to simplify the conditional mean estimation of the rate given the SAD and QP values. This information is stored into three look-up tables depending on picture types. We use these tables to find the optimal QP values in least-mean-square (LMS) sense for a given bit budget of the current frame. Simulation results show that the proposed algorithm outperforms the informative MPEG-4 rate control algorithm in terms of reproduced image quality and coding efficiency while requiring much less implementation complexity. Most of all it keeps the bit rate very close to the required bit-rate due to the accuracy of the conditional mean estimator that solves the nonlinear R-D function.