Perspective motion is commonly represented in video content that is captured and compressed for various applications including cloud gaming, vehicle and aerial monitoring, etc. Existing approaches based on an eight-parameter homography motion model cannot deal with this efficiently, either due to low prediction accuracy or excessive bit rate overhead. In this paper, we consider the camera motion model and scene structure in such video content and propose a joint global and local homography motion coding approach for video with perspective motion. The camera motion is estimated by a computer vision approach, and camera intrinsic and extrinsic parameters are globally coded at the frame level. The scene is modeled as piece-wise planes, and three plane parameters are coded at the block level. Fast gradient-based approaches are employed to search for the plane parameters for each block region. In this way, improved prediction accuracy and low bit costs are achieved. Experimental results based on the HEVC test model show that up to 9.1% bit rate savings can be achieved (with equal PSNR quality) on test video content with perspective motion. Test sequences for the example applications showed a bit rate savings ranging from 3.7 to 9.1%.<\/p>\n","protected":false},"excerpt":{"rendered":"
Perspective motion is commonly represented in video content that is captured and compressed for various applications including cloud gaming, vehicle and aerial monitoring, etc. Existing approaches based on an eight-parameter homography motion model cannot deal with this efficiently, either due to low prediction accuracy or excessive bit rate overhead. 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