| dc.description.abstract | With the advancement of technology, the quality and resolution of images have also increased, and the amount of data becomes larger and larger. HEVC (High Efficiency Video Coding), also known as H.265, uses newer technology to reduce the bit rate, and the associated Improve coding computational complexity. This paper uses the convolutional neural network (CNN) and the support vector machine (SVM) in deep learning and machine learning that have flourished in recent years to apply it to HEVC coding unit decision-making. At the beginning of encoding, SVM is first used to classify the coding unit depth and prediction unit mode, and a CTU is classified into four categories: depth 0, depth 0~1, depth 0~2, and depth 0~3, and then the convolutional neural network is used. The network layer is subdivided downward. By using HEVC′s recursive operation to process coding units, subsequent coding calculations are terminated early at a specific depth. In addition, the decision threshold of the support vector machine is used to reduce the number of entries into the convolutional neural network through specific conditions to save coding time. The overall average BDBR increases to 4.72%, and the encoding time can be saved by 75.22% on average. Discuss the performance comparison between Random Access and Low Delay architectures, and the impact of different GOP size. | en_US |