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A LOW LATENCY CLOUD GAMING SYSTEM USING EDGE PRESERVED IMAGE HOMOGRAPHY

机译:使用边缘保留图像识别的低延迟云游戏系统

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The emerging cloud gaming technology has been growing fast, driving up huge mobile consumer demands. The video streaming based cloud gaming scenario renders the game scenes in the cloud servers, and streams the encoded sequences to the thin clints where the game scenes are decoded and displayed to the players. However, current existing clouding gaming services have some problems, such as the latency and bandwidth limitation. The size of the video stream is usually quite large which requires heavy transmission. Worse still, the frame data rate will burst when the game scenes contain fast translation or rotation, resulting in strong latency problem. In this paper, we propose a novel video streaming based cloud gaming algorithm which reduces the burst of the frame rate significantly. There are mainly two innovations in this paper. Firstly, based on the analysis of the motion estimation strategy in the video codec, we introduce image homography technique for better motion prediction. Meanwhile, according to the rasterization rules of the game engine, we present a special designed interpolation algorithm named Edge Preserved Interpolation (EPI), for more accurate edge interpolation and further reduce the residues in the edge regions. The proposed algorithm is implemented on the ×264 platform. Experimental results show that our algorithm has 18.0% BD-rate reduction compared with ×264.
机译:新兴的云游戏技术一直在快速增长,推动巨大的移动消费者需求。基于视频流的云游戏场景使云服务器中的游戏场景呈现,并将编码的序列流传送到游戏场景被解码并将其显示给玩家的细则。但是,当前现有的云游戏服务有一些问题,例如延迟和带宽限制。视频流的大小通常非常大,这需要沉重的传输。更糟糕的是,当游戏场景包含快速的翻译或旋转时,帧数据速率将突发,从而产生强烈的延迟问题。在本文中,我们提出了一种新的基于视频流的云游戏算法,其显着降低了帧速率的突发。本文主要有两种创新。首先,基于视频编解码器中的运动估计策略的分析,我们引入了更好的运动预测的图像配备技术。同时,根据游戏引擎的光栅化规则,我们介绍了一个名为Edge保留插值(EPI)的特殊设计的插值算法,用于更精确的边缘插值,并进一步减少边缘区域中的残留物。所提出的算法在×264平台上实现。实验结果表明,与×264相比,我们的算法具有18.0%的BD速率降低。

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