| Popis: |
In order to improve the performance of virtual reality video intraframe prediction coding,convolutional neural network algorithm is used to select video frame coding unit(CU) to reduce the complexity of video image coding.Firstly,quantization parameters are set to obtain the virtual reality video frame samples,then the image coding tree is constructed,and the convolutional neural network (CNN) frame coding unit optimization model is established.The image brightness of frame samples is taken as the CNN input,combined with the image rate distortion cost threshold,the optimization results of the frame coding unit are obtained through training.Using CNN training optimization,the coding tree(CTU) structure with different depths and an appro-priate number of CU modules can be obtained according to the intraframe coding requirements of different texture modules of the image.Experiments show that,by reasonably setting the convolution kernel size and quantization parameters,CNN algorithm can obtain better image quality and less coding time than common video intraframe prediction coding algorithms. |
