Optimization For Streaming Video Coding And Fast Transcoding Technology Based On HEVC

With the rapid development of the Internet,the popularity of multimedia technologies,especially network streaming media,has made digital video applications more and more widely available.In this situation,a large amount of digital video needs to be transmitted and stored every moment,which is a big test for storage space and network bandwidth.Video coding technology has been continuously developed to ensure video quality with as little space as possible while storing video,and a series of video coding standards have been released,such as MPEG-X series and H.26 X series.Currently,H.264/AVC is most used in all video coding standards.HEVC,the latest video coding standard,has not yet been widely used,but due to its excellent compression efficiency,it will surely become the main video coding standard in the future.The optimization for HEVC has also been a hot topic in recent years.At present,online streaming media has gradually become an essential part of people's lives.Different from the traditional broadcast and other video transmission systems,the network streaming media video is generally transmitted in accordance with a fixed-length segment,so as to achieve its purpose of adaptively adjusting the video resolution and bit rate according to the user's network environment.In this situation,there are new requirements for the rate control of video coding.It requires that the number of bits of each video segment after encoding is as identical as possible and satisfies the target bit rate,regardless of the bit rate fluctuation within the segment.The current ABR rate control algorithms in the most commonly used open source encoders such as x264 or x265 do not meet this requirement.For this reason,this paper introduces the R-? model in the CBR rate control on the basis of the default ABR algorithm of x265,and combines them to propose an ABR rate control algorithm based on segment.Compared with the original ABR algorithm of x265,proposed algorithm can effectively reduce the rate fluctuation at the segment level,and there is no obvious change in encoding time and video quality.In addition,H.264/AVC is still the most widely used video coding standard,including network streaming video.In the process of gradual application of HEVC,there is a great deal of demand for transcoding H.264/AVC encoded video into HEVC encoded video.However,due to the high complexity of HEVC coding,direct transcoding can take a lot of time.For this reason,this paper proposes a fast transcoding algorithm,which uses Naive Bayesian machine learning method to predict the CU partition in the HEVC encoding process through information extracted from the H.264/AVC code stream,thereby reducing coding complexity,which in turn accelerates transcoding.This paper also tests and analyzes the performance of the proposed algorithm.