摘要(英) |
Nowadays,with the rapid development of technology,people ′s life is inseparable from technological products, and the pursuit of images is gradually improving. However,as the resolution of images becomes higher and higher,the burden is undoubtedly a huge amount of data transmission.
In order to compress these images more effectively,the compression technology used by HEVC(High Efficiency Video Coding) can increase the compression rate about twice as much as that of the previous generation of compression standards. However,the image will produce irreversible distortion at the same time of encoding and compressing. How to make the distorted image as close to the original image as possible while saving time is the focus of research.
In recent years, there have been many studies on the application of deep learning in HEVC to enhance image quality. In this paper, two topics are proposed to enhance image quality by post-processing for HEVC Intra prediction. The first one is Gaussian mask,the method provides additional information to the CNN model. Compared with the HEVC reference program HM-16.0,it can increase the BDPSNR by 0.285 (dB) and reduce the BDBR by 5.16 (%).The second method is to further improve the model performance by using the ResNet architecture. It can increase Y-BDPSNR of 0.319 (dB) and decrease Y-BDBR of 5.79 (%). |
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