Adaptive bit-plane selection-based low complexity motion estimation for screen content coding

Screen content video coding has become an emerging research topic with the spread of applications suchas cloud gaming, screen/desktop virtualization, and mobile or external display interfacing. Screen content videoshave different features compared to conventional camcorder-captured scenes. In this work, a novel low bit-depthrepresentation-based motion estimation approach is proposed to exploit screen content specific features to improvecoding efficiency. The proposed approach is based on an adaptive selection of Gray-coded bit-planes in order to generatelow bit-depth representation of original screen content frames. The experimental results show that the motion estimationperformance of the proposed approach is significantly better compared to the methods in the same category for screencontent videos.

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