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An SMVQ compressed data hiding scheme based on multiple linear regression prediction

机译:基于多元线性回归预测的SMVQ压缩数据隐藏方案

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摘要

In this paper, we propose a side matching vector quantisation (SMVQ) data hiding scheme for image using multiple linear regression prediction. For each pixel block, the proposed scheme combines the multiple linear regression algorithm and the SMVQ algorithm, so that it can more accurately match the codeword or directly obtain the predicted value closer to the real pixel. Our experimental results show that when the VQ codebook sizes are 128, 256, 512, and 1024, and the SCB size is 16, this scheme obtains a better compression rate and information embedding ability. It can be concluded from the experimental results that this scheme is superior to existing algorithms in terms of compression and embedding capacity.
机译:在本文中,我们提出了一种使用多元线性回归预测的图像的侧面匹配矢量量化(SMVQ)数据隐藏方案。 对于每个像素块,所提出的方案组合了多元线性回归算法和SMVQ算法,使得它可以更准确地匹配码字或直接获得更靠近真实像素的预测值。 我们的实验结果表明,当VQ码本尺寸为128,256,512和1024时,SCB尺寸为16时,该方案获得更好的压缩率和信息嵌入能力。 从实验结果可以得出结论,该方案在压缩和嵌入容量方面优于现有的现有算法。

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