Neural Network-Based Scalable Fast Intra Prediction Algorithm in H.264 Encoder

Abstract

In this paper, we propose a neural network-based scalable fast intra prediction algorithm in H.264 in order to reduce redundant calculation time by selecting the best mode of 4 × 4 and 16 × 16 intra prediction. In this reason, it is possible to encode compulsively by 4 × 4 intra prediction mode for current MB(macro block)'s best prediction mode without redundant mode decision calculation in accordance with neural network's output resulted from corelation of adjacent encoded four left, up-left, up and up-right blocks. If there is any one of MBs encoded by 16 × 16 intra prediction among four MBs adjacent to current MB, the probability of re-prediction into 16 × 16 intra prediction will become high. We can apply neural networks in order to decide whether to force into 4 × 4 intra prediction mode or not. We can also control both the bit rates and calculation time by modulating refresh factors and weights of neural network's output depend on error back-propagation, which is called refreshing. In case of encoding several video sequences by the proposed algorithm, the total encoding time of 30 input I frames are reduced by 20% ∼ 65% depending upon the test vector compared with JM 8.4 by using neural networks and by modulating scalable refreshing factor. On the other hand, total encoding bits are increased by 0.8% ∼ 2.0% at the cost of reduced SNR of 0.01 dB.