A Multi-scale-multi-input Complementation Classification Network for Fast Coding Tree Unit Partition
Deep Neural Networks(DNN)have been widely applied to Coding Tree Unit(CTU)partition of intra-mode High Efficiency Video Coding(HEVC)for reducing the HEVC encoding complexity,however,existing DNN-based CTU partition methods always neglect the correlation of features between Coding Units(CU)at different scales and suffer from the accumulation of classification errors.Therefore,in this paper,a Multi-scale-multi-input Complementation Classification Network(MCCN)for faster and more accurate CTU partition is proposed.First,a Multi-scale Multi-input Convolutional Neural Network(MMCNN)is proposed,which builds up the correlation of features between CUs at different scales by fusing multi-scale CU features.Therefore,our MMCNN possess more powerful representation abilities.Second,a Complementary Classification Strategy(CCS)is proposed,in which the final depth prediction results for each CU are determined by combining the results of multi-classification with the results of binary classification and triplex classification with the voting mechanism.The proposed CCS avoids the accumulation of classification errors and achieves more accurate CTU partition.Extensive experiments demonstrate that our MCCN achieves lower HEVC encoding complexity and more accurate CTU partition:reduce the average encoding complexity by 71.49%only at the cost of a 3.18%average Bjøntegaard Delta Bit-Rate(BD-BR).And the average accuracies of 32×32 CU depth prediction and 16×16 CU depth prediction are increased by 0.65%~0.93%and 2.14%~9.27%respectively.
Deep Neural Networks(DNN)Intra-mode High Efficiency Video Coding(HEVC)Features RepresentationCoding Tree Unit(CTU)partitionMulti-scale-multi-inputComplementation classification