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Efficient DC term encoding scheme based on double prediction algorithms and Pareto probability models

机译:基于双重预测算法和帕累托概率模型的有效直流项编码方案

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In this paper, a new algorithm which adopts the techniques of double prediction and the Pareto probability model was applied to encode the DC term in the JPEG compression process. Conventionally, the DC term was encoded by differential coding, i.e., the difference of the DC values between the current block and the previous block. In this paper, we first use the DC terms of four adjacent blocks to predict the current DC value. We then further use the prediction error of the four adjacent blocks to estimate the variance of the prediction error of the current block. We call it the double prediction algorithm. Next, the Pareto distribution is applied to model the probability distribution of the prediction error. Simulation results show that, with the proposed algorithms, the data size required for DC terms is significantly reduced by 25% ∼ 60% and a much higher compression rate can be achieved.
机译:本文采用了一种采用双重预测技术和帕累托概率模型的新算法,对JPEG压缩过程中的直流项进行编码。按照惯例,DC项是通过差分编码来编码的,即,当前块与先前块之间的DC值之差。在本文中,我们首先使用四个相邻块的直流项来预测当前的直流值。然后,我们进一步使用四个相邻块的预测误差来估计当前块的预测误差的方差。我们称其为双重预测算法。接下来,使用帕累托分布对预测误差的概率分布进行建模。仿真结果表明,利用所提出的算法,DC项所需的数据量显着减少了25%到60%,并且可以实现更高的压缩率。

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