All-zero block detection algorithm in H.264 based on radial basis function network

Abstract

In this paper,a kind of algorithm for all-zero block detection based on Radial Basis Function(RBF) Neural Network(NN) was proposed to improve the accuracy of all-zero block detection algorithm.By analyzing the H.264 encoder features,six effective features were selected,including Sum of Absolute Difference(SAD),Sum of Absolute Transformed Difference(SATD),block type,Rate Distortion Optimization(RDO) cost,Quantization Parameter(QP) and the situation of reference block.Considering the SATD should be used in the Hadamard Transform(HT),to get the relationship of QP and RBF network width parameter through the least square method,the algorithm used two classifiers to separate all-zero blocks from non-all-zero blocks based on the encoding situation of the reference block.This algorithm could improve coding speed over 50% on average while keeping bit rate and video quality almost unchanged.The experimental results show that the proposed algorithm can improve all-zero block detection accuracy effectively and coding efficiency based on NN.