An IBP-CNN Based Fast Block Partition For Intra Prediction

Abstract

The increase of block size to 64×64 in HEVC leads to the increase of computational complexity of intra prediction. The convolution neural network (CNN) shows advantages in the extraction and application of image features than the traditional intra prediction optimization algorithm which is developed manually. For reducing the computational complexity of intra prediction, a CNN-based algorithm, intra block partition CNN (IBP-CNN) is proposed in this paper to get the block partition. First, a database which is consisted of coding tree unit (CTU) images and label images is established. The position of pixels in the label images are consistent with those in CTU images. Second, the texture features are analyzed by IBP-CNN to get the block partition. Then the output of the network is adjusted according to the quadtree structure of HEVC to facilitate the calculation of rate distortion (RD) cost. The method proposed in this paper reduces the average coding time of about 59.07% and the average BD-rate is about 1.55%.