摘要 : In this paper, we present a novel context-adaptive model parameter prediction scheme for improving the estimation accuracy of the mean absolute difference (MAD) of texture and other model parameters in the linear rate quantizatio... 展开 In this paper, we present a novel context-adaptive model parameter prediction scheme for improving the estimation accuracy of the mean absolute difference (MAD) of texture and other model parameters in the linear rate quantization (R-Q) model-based H.264/AVC macroblock layer rate control for low bit rate real-time applications. The context-adaptive prediction scheme simultaneously exploits both spatial correlations and temporal correlations among the neighboring macroblocks within a so-called context of a macroblock. The location and shape of the context as well as the number of neighboring macroblocks in the context are adaptively computed according to local video signal characteristics using a Manhattan distance metric and an improved 2-D sliding window method. The proposed context-adaptive model parameter prediction scheme effectively suppresses the detrimental oscillations of estimated model parameters. Extensive experiments show that compared to the recent H.264/AVC reference software, macroblock layer rate control algorithm using our proposed context-adaptive prediction scheme significantly improves the MAD and model parameter prediction accuracy and bit achievement accuracy, and hence obtains much better rate distortion performance. 收起