Cloud Game Video Coding Based On Human Eye Fixation Point

Cloud Gaming enables users to run high-quality games on thin clients with limited graphics processing and data computing capabilities. Under the running mode of cloud games, all games are run on the server side, and the rendered game picture is compressed and sent to the user through the network. On the client side, the user's gaming device doesn't need any high-end processors or graphics cards, only basic video extraction capabilities. However, cloud gaming requires a high bandwidth connection to present a good-quality game picture to the user. At present, the main problem is limited bandwidth during transmission, which leads to poor quality of the game image received by users and poor user experience, which has become an important problem hindering the popularity of cloud games. To solve this problem, we observed that when playing a game, the user's eyes are not always focused on the entire picture, and due to the characteristics of visual perception, the user pays more attention to the area around the eye fixation point. In this paper, for the first time, we add information about user interactions with devices to the network to more accurately predict user fixation points. Using visual perception features, more bit rates are assigned to ROI regions near the user's fixation point. Our results show that our network is able to predict more accurate user fixation points, and that our approach can significantly improve the subjective quality of the area near the user's focus of the game frame at the same bitrate, with the VMAF scores is 2 to 3 points higher on average compared to H.264, the most commonly used standard encoder in cloud games today.