Department of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran.
IEEE Trans Image Process. 2010 Jun;19(6):1504-17. doi: 10.1109/TIP.2010.2042646. Epub 2010 Mar 15.
In this paper, a novel arrangement for quantizer levels in the Quantization Index Modulation (QIM) method is proposed. Due to perceptual advantages of logarithmic quantization, and in order to solve the problems of a previous logarithmic quantization-based method, we used the compression function of mu-Law standard for quantization. In this regard, the host signal is first transformed into the logarithmic domain using the mu-Law compression function. Then, the transformed data is quantized uniformly and the result is transformed back to the original domain using the inverse function. The scalar method is then extended to vector quantization. For this, the magnitude of each host vector is quantized on the surface of hyperspheres which follow logarithmic radii. Optimum parameter mu for both scalar and vector cases is calculated according to the host signal distribution. Moreover, inclusion of a secret key in the proposed method, similar to the dither modulation in QIM, is introduced. Performance of the proposed method in both cases is analyzed and the analytical derivations are verified through extensive simulations on artificial signals. The method is also simulated on real images and its performance is compared with previous scalar and vector quantization-based methods. Results show that this method features stronger a watermark in comparison with conventional QIM and, as a result, has better performance while it does not suffer from the drawbacks of a previously proposed logarithmic quantization algorithm.
本文提出了一种在量化索引调制(QIM)方法中量化电平的新方法。由于对数量化的感知优势，并且为了解决以前基于对数量化的方法的问题，我们使用了 mu 律标准的压缩函数进行量化。在这方面，首先使用 mu 律压缩函数将主机信号转换为对数域。然后，对转换后的数据进行均匀量化，并使用逆函数将结果转换回原始域。然后将标量方法扩展到矢量量化。为此，在遵循对数半径的超球面上量化每个主机矢量的大小。根据主机信号分布计算标量和矢量情况的最佳参数 mu。此外，类似于 QIM 中的抖动调制，在提出的方法中引入了秘密密钥的包含。对两种情况下的提议方法的性能进行了分析，并通过在人工信号上进行广泛的仿真验证了分析推导。还对真实图像进行了仿真，并将其性能与以前的基于标量和矢量量化的方法进行了比较。结果表明，与传统的 QIM 相比，该方法的水印更强，因此性能更好，而不会遭受以前提出的对数量化算法的缺点。
