Research On Low Complexity And Strong Network Adaptability Coding Based On High Efficiency Video Coding

Since the popularity of high-definition and ultra-high-definition video application,the amount of video data has explosively increased.With the condition of limited storage resources,H.264/AVC video coding standard has gradually become difficult to cope with the demand for efficient video storage and transmission,which is a widely used video coding standard.Therefore,a new generation video coding standard(HEVC)that focuses on "higher compression efficiency" and "higher reconstruction quality" has developed.As an upgrade for H.264/AVC,HEVC introduces many advanced coding technologies.And then it achieves 50% bitrate reduction under the equivalent visual quality.However,in the journey of HEVC to large-scale practicalization,its coding speed and network adaptability still need to be improved.In this paper,the HEVC standard is used as the research platform,and the low complexity intra coding and rate control optimization technology is taken as the research goal,which will strive to innovate in low complexity coding and strong network adaptive coding at the theoretical research and application practice.Herein,the main research contents and contributions are as follows:1.A fast coding unit(CU)depth decision algorithm based on convolutional neural network(CNN)for HEVC intra coding is proposed.To predict CU depth range,this paper designs a convolution neural network architecture for HEVC intra depth range decision-making based on the relationship between CU depth and its texture complexity.Compared with HM-16.9,this fast algorithm can accelerate intra coding process while maintaining the accuracy of CU partitioning.2.A spatial-temporal correlation based fast intra prediction unit(PU)mode decision is proposed.To simplify candidate mode set reasonably,this paper analyzes the correlation between the current PU optimal mode and the optimal mode of the spatial-temporal adjacent PU,then designs a rough mode decision candidate mode set simplification based on spatial correlation,and a rate distortion optimization candidate mode set simplification strategy based on temporal correlation.Compared with HM-16.9,this fast algorithm can reduce intra coding complexity without losing vide reconstruction quality.Further,the fusion of CU depth fast selection and PU mode fast decision for intra coding is realized,which achieves more coding complexity re-duction.3.An adaptive model parameters prediction mechanism for large coding unit level rate control is proposed.Herein,the content similarity between frames is explored to determine the best associated frame,which has been encoded and has smallest difference from the current encoding frame.Further,according to the correlation between model parameters and video content,this paper designs the adaptive parameter prediction mechanism based on the encode information in the best associated frame.It successfully avoid model parameters updating based on empirical value in HEVC,which will cause large rate control error in the initial stage of coding.Compared with HM-16.9,this rate control optimal algorithm can achieve synchronous lifting in bitrate accuracy and rate distortion performance without introducing additional computation.