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Improving motion vector prediction using linear regression

机译:使用线性回归改进运动矢量预测

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摘要

The motion vectors take a large portion of the H.264/AVC encoded bitstream. This video coding standard employs predictive coding to minimize the amount of motion vector information to be transmitted. However, the motion vectors still accounts for around 40% of the transmitted bitstream, which suggests further research in this area. This paper presents an algorithm which employs a feature selection process to select the neighboring motion vectors which are most suitable to predict the motion vectors mv being encoded. The selected motion vectors are then used to approximate mv using Linear Regression. Simulation results have indicated a reduction in Mean Squared Error (MSE) of around 22% which results in reducing the residual error of the predictive coded motion vectors. This suggests that higher compression efficiencies can be achieved using the proposed Linear Regression based motion vector predictor.
机译:运动矢量占据了H.264 / AVC编码比特流的很大一部分。该视频编码标准采用预测编码以最小化要发送的运动矢量信息的量。但是,运动矢量仍占传输比特流的40%左右,这表明该领域需要进一步研究。本文提出了一种算法,该算法采用特征选择过程来选择最适合预测正在编码的运动矢量mv的相邻运动矢量。然后,使用线性回归将选定的运动矢量用于近似mv。仿真结果表明,均方误差(MSE)降低了约22%,从而降低了预测编码运动矢量的残留误差。这表明使用建议的基于线性回归的运动矢量预测器可以实现更高的压缩效率。

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