| dc.description.abstract | In today′s society, we have higher and higher requirements for resolution. In order to meet our needs for high-resolution images, High-Efficiency Video Coding(HEVC) can be twice as compressed as the previous generation of video coding. Because in the compression technology of HEVC, coding units, prediction units, transform units, and quantization methods are used. In terms of network transmission, in order to make the transmitted image have lower distortion and better performance, rate control is the basic element actually used in the video coding standard. Rate control scheme typically builds a model that characterizes the relationship between Bitrate and a coding parameter, e.g. quantization parameters and Lagrange multiplier(λ). For inter prediction, the parameters can be accurately updated based on the information of the previously coded image to adapt to the content of the video, however for intra prediction, it’s a challenge. In this paper, in order to enable a more accurate rate control method for intra prediction, in addition to quoting the convolutional neural network(CNN) to predict the parameters of each coding tree unit, the features of the support vector machine(SVM) model are also used to classify the training data. After distinguishing smooth and complex blocks through the classification of training data, making CNN model training is more accurate. The experimental results show that the method based on CNN and SVM reduces the bitrate error by 0.677% compared with The rate control method of HM 16.0 in HEVC and the coding performance is also increased by 0.78%. | en_US |