| 摘要(英) |
The covid-19 virus has spread all over the world in recent years. It has infected 500 million people and killed more than 6 million people, and the number is still rising; Taiwan is facing the epidemic, which has also caused tens of thousands of people to be diagnosed and need to be isolated at home. In order to reduce the risk of infection, schools have changed to online teaching; most of all the companies have also changed to online meetings to avoid gatherings. Limited by the screen resolution, system processing capability, storage capacity, and network bandwidth viewed by the user, it becomes a very important issue in transmitting high-quality or ultra-high-quality image compression technology. Reduce latency or distortion of the image and complete transmission of the sound to the other party at the same time is a very hot research topic.
The new generation of HEVC (High Efficiency Video Coding) compression standard can achieve 50% the compression efficiency compared to the previous generation H.264 in the same image quality. But the encoding operations are very complicated and cause irreversible image distortion. Distortion compensation is one of the methods. This paper continues the post-processing research, using the spatial domain correlation between pixels to extract image features, including Gaussian Mask and Residual algorithms, and enrolls the information theory in Introduces multiple side information structures. In this paper, we propose two CNN (Convolutional Neural Networks) models, VDSR (Very Deep Super-Resolution) and EDSR (Enhanced Deep Super-Solution) as the main architecture, to discuss the effect of Inter Prediction on encoder post-process under different CNN architectures. In the evaluation of processing efficiency, the CNN model is trained with different algorithm features. The image test results of the CNN with VDSR as the main structure have significantly improved the image quality of dual-input and triple-input, Forthmore, the test results of the CNN with the EDSR main structure, The results of SVM (Super Vector Machine) classification are also significantly better than the former, BDBR is reduced by about 0.381%~0.701%, and BDPSNR is improved by about 0.037~0.171db. It is particularly noteworthy that when the two architectures are in the same bitrate, EDSR’s BD-PSNR can be nearly doubled. |
| 參考文獻 |
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