Research On QSHVC Inter-Frame Fast Algorithm Based On Mode And Depth Probability

In recent years,with the advent of the information network era,video has become the main channel for users to obtain knowledge and information.However,since the amount of video data is very large,it must be effectively compressed and coded in order to store and deliver the video data efficiently,so the High Efficiency Video Coding(HEVC)standard has been proposed.Due to the diversity of network types,the video signal may fluctuate even at the same network bandwidth,which requires the video stream to adapt to different bandwidth requirements.Scalable High Efficiency Video Coding(SHVC),which is based on HEVC,can effectively solve this problem,but the coding process of the scalable video coding standard is very complicated because there are basic layers and one or more enhancement layers in the scalable video coding framework,and it is necessary to encode the basic layer and However,the quality of SHVC-based scalable video coding requires the use of different coding methods for both basic and enhanced layers,which makes the coding complexity even higher,and this seriously affects the wide application of SHVC.Therefore,it is very important to study the fast coding algorithm of SHVC for scalable video coding.In this thesis,we mainly focus on the quality scalable video coding inter-frame prediction fast algorithm,because the study of quality scalable video coding inter-frame fast algorithm includes the prediction algorithm for coding pattern and the prediction algorithm for coding depth,so this thesis proposes two algorithms for mode and depth,the main algorithm research is as follows.1.A likelihood-based pattern prediction algorithm is proposed.Firstly,the possibility of using different coding modes in the current coding unit is calculated based on the depth correlation between the current coding unit and the biparental coding unit,the spatial correlation with the neighboring coding units and the inter-layer correlation with the basic layer coding units,and then the mode selection is performed by combining the inter-layer correlation and spatial correlation.The experimental results show that the encoding time of the enhanced layer is reduced by 65% on average with an average increase of 1.07% in BDBR.2.A depth prediction algorithm based on the likelihood size is proposed.Firstly,the probability size of the current coding unit depth is calculated based on temporal and spatial correlation,and the coding depths with smaller probability are excluded,and then the depth is terminated early using Gaussian mixture model GMM and maximum expectation clustering EM in combination with the rate distortion value.The experimental results show that the proposed algorithm is able to significantly improve the coding speed with an average of 31% less coding time with 0.17% increase in BDBR.