在科技迅速發展的現今，人們的生活與科技產品形影不離，對於影像方面的追求也逐步提升，但隨著影像解析度越來越高的同時，所需負擔的無疑是龐大的資料傳輸量，為了更有效的對這些影像進行壓縮，HEVC(High Efficiency Video Coding)使用的壓縮技術能比上一代的壓縮標準提高約兩倍的壓縮率，但是在編碼壓縮的同時，影像會產生不可逆的失真，如何在節省時間的同時，讓失真影像盡可能地接近原始影像正是研究的重點。 近幾年也有許多研究是以深度學習應用於HEVC中增強影像品質，本論文是在HEVC畫面內預測中以後處理的方式提出了二個主題來增強影像品質，第一種是以高斯遮罩的方式提供網路模型額外資訊，與HEVC參考程式HM-16.0相比可以提升0.285(dB)的BDPSNR與降低5.16(%)的BDBR，第二種則是以ResNet架構的方式使模型性能進一步提升，可以提升0.319(dB)的Y-BDPSNR與降低5.79(%)的Y-BDBR。 ;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 (%).
