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Linear modeling for MPEG-4 intra frame decoding complexity prediction based on statistical analysis

机译：基于统计分析的MPEG-4帧内解码复杂度预测的线性建模

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摘要

Video decoding complexity prediction plays an important role in energy efficient applications, such as dynamic voltage scaling and workload reshaping. This paper presents a novel linear model for MPEG-4 intra frame decoding complexity prediction. Detailed experiments are conducted to exploit the statistical relationship between frame length and decoding complexity for various video contents under different bitrates. The experiments show that decoding complexity is linear related to frame length, the parameters of linear model vary slightly in terms of video sequences and bitrates, and the model parameters for different size video are proportional to the ratio of video size. Based on above principles, the linear model for CIF format video are fitted offline and utilized to predict both CIF and 4CIF format video sequences' intra frame decoding complexity on the fly. The probability density function of prediction error appeared normal distributed and the average prediction error is 0.47%. The maximal prediction error is 2.94% and the runtime overload of the proposed method is 54 cycles/frame on TI TMS320DM642 platform.
机译：视频解码复杂度预测在节能应用中起着重要作用，例如动态电压缩放和工作量整形。本文提出了一种用于MPEG-4帧内解码复杂度预测的新型线性模型。进行了详细的实验，以探索在不同比特率下各种视频内容的帧长与解码复杂度之间的统计关系。实验表明，解码复杂度与帧长成线性关系，线性模型的参数在视频序列和比特率方面略有变化，不同大小视频的模型参数与视频大小的比例成正比。基于上述原理，离线拟合CIF格式视频的线性模型，并用于动态预测CIF和4CIF格式视频序列的帧内解码复杂度。预测误差的概率密度函数呈正态分布，平均预测误差为0.47％。在TI TMS320DM642平台上，最大预测误差为2.94％，所建议的方法的运行时过载为54个周期/帧。

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