VQ based data hiding method for still images by tree-structured links

Hisashi Igarashi, Yuichi Tanaka, Madoka Hasegawa, Shigeo Kato
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Abstract

In this paper, we propose a data embedding method into still images based on Vector Quantization (VQ). In recent years, several VQ-based data embedding methods have been proposed. For examle, ‘Mean Gray-Level Embedding method (MGLE)’ are ‘Pair wise Nearest-Neighbor Embedding method (PNNE)’ are simple, but not sufficiently effective. Meanwhile, an efficient adaptive data hiding method called ‘Adaptive Clustering Embedding method (ACE)’ was proposed, but is somewhat complicated because the VQ indices have to be adaptively clustered in the embedding process. In our proposed method, output vectors are considered as nodes, and nodes are linked as a tree structure and information is embedded by using some of linked vectors. The simulation results show that our proposed method indicates higher SNR than the conventional methods under the same amounts of embedded data.
基于VQ的树状链接静态图像数据隐藏方法
本文提出了一种基于矢量量化(VQ)的静态图像数据嵌入方法。近年来,人们提出了几种基于vq的数据嵌入方法。例如,“平均灰度嵌入法(MGLE)”和“成对最近邻嵌入法(PNNE)”都很简单,但不够有效。同时,提出了一种高效的自适应数据隐藏方法“自适应聚类嵌入法”(ACE),但由于嵌入过程中需要对VQ指标进行自适应聚类,该方法比较复杂。在我们提出的方法中,输出向量被认为是节点,节点被链接成一个树结构,并通过一些链接向量嵌入信息。仿真结果表明,在相同的嵌入数据量下,该方法比传统方法具有更高的信噪比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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