基于自适应量化嵌入器的感知隐写

Guangjie Liu, Yue-wei Dai, Zhiquan Wang, Shiguo Lian
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引用次数: 0

摘要

提出了一种基于自适应量化嵌入器的隐写方案。该方案利用新构造的对比相关失真度量来优化图像块的量化步骤,以保证在繁忙区域嵌入更多的数据。与现有方法不同的是,我们的方案将AQE参数与信息位一起嵌入到封面图像中。仿真实验表明,该方案在感知能力和容量之间取得了很好的平衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Perceptual Steganography Based on Adaptive Quantization-Embedder
A steganographic scheme based on adaptive quantization-embedder is proposed in this paper. The scheme uses the new constructed contrast-correlation distortion metric to optimally choose quantization steps for image blocks to guarantee more data being embedded in busy areas. Different form existing methods, our scheme embeds the AQE parameters together with message bits into the cover image. Simulation experiments show the proposed scheme can provide a good trade-off between the perceptibility and the capacity.
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