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A fast and efficient reversible watermarking method using generalized integer transform

机译:利用广义整数变换的快速高效的可逆水印方法

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The earliest reversible watermarking using generalized integer transform proposed by Alattar [3] has been concerned and expanded because this technique can achieve high embedding capacity without losing the implication of the cover difference expansion (DE) method. According to Alattar's technique, n-1 bits will be embedded into a vector with n pixels if it is expandable. The larger the vector size is, the higher the embedding capacity is. Weng et el. [16] improved Alattar' scheme to achieve the higher capacity. The evaluation of vector expandability or a scheme changeability based on the idea of Alattar (called the Alattar's method) and Weng based method have exponential complexity in the length of the vector, so calculation time is inversely proportional complexity of the embedding capacity. Alattar or Weng based methods only considered the vector with 3 or 4 pixels for that drawback. For larger vectors, example a vector in size of 16, it is divided into 4 sub-vectors in size of 4 and 3 bits could be embedded in each subvector. Therefore, only 12 bits were embedded into a vector with 16 pixels. Thus, the embedding ability didn't gain as theoretical analysis. This paper proposes the expandable and changeable criteria for a vector with linear complexity according to the length of vector. Since, the embedding capacity of Alattar or Weng based methods is really obtained as theoretical analysis that means the embedding capacity of those schemes are better. Experimental results show that schemes applied to the proposed algorithms to check the expandable and changeable of vector is fast and it outperforms several widely used schemes in terms of computational complexity.
机译:Alattar [3]提出的最早使用广义整数变换的可逆水印技术已经得到关注和扩展,因为该技术可以实现高嵌入能力,而又不会失去覆盖差异扩展法的含义。根据Alattar的技术,如果n-1位是可扩展的,则将其嵌入具有n个像素的向量中。向量大小越大,嵌入能力越高。翁等。 [16]改进了Alattar方案以实现更高的容量。基于Alattar的思想(称为Alattar方法)和基于Weng的方法,对向量的可扩展性或方案可变性的评估在向量的长度上具有指数复杂度,因此计算时间与嵌入能力成反比。基于Alattar或Weng的方法仅考虑具有3个或4个像素的矢量来解决此缺点。对于较大的向量,例如大小为16的向量,将其分为大小为4的4个子向量,并且可以在每个子向量中嵌入3位。因此,只有12位被嵌入到具有16个像素的向量中。因此,嵌入能力没有得到理论分析。本文根据向量的长度,提出了线性复杂度向量的可扩展性和可变性准则。因为,基于Alattar或Weng的方法的嵌入能力实际上是通过理论分析获得的,这意味着这些方案的嵌入能力更好。实验结果表明,所提出的算法用于检查向量的可扩展性和可变性的方案是快速的,并且在计算复杂度方面优于几种广泛使用的方案。

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