Image Watermarking Algorithm Based on Quantization and Relationship

XIONG Xiang-guang

Chinese

您当前的位置：

首页 >

文章列表页 >

Image Watermarking Algorithm Based on Quantization and Relationship

更新时间：2026-09-04

- Image Watermarking Algorithm Based on Quantization and Relationship
- Vol. 30, Issue 9, Pages: 71-74(2013)
- 作者机构：
  
  贵州师范大学机构数学与计算机科学学院,贵州,贵阳,550001
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2013
- 稿件说明：
移动端阅览
熊祥光. 基于量化和关系的图像水印算法[J]. 微电子学与计算机, 2013, 30(9): 71-74.

XIONG Xiang-guang. Image Watermarking Algorithm Based on Quantization and Relationship[J]. 2013, 30(9): 71-74.
熊祥光. 基于量化和关系的图像水印算法[J]. 微电子学与计算机, 2013, 30(9): 71-74. DOI：

XIONG Xiang-guang. Image Watermarking Algorithm Based on Quantization and Relationship[J]. 2013, 30(9): 71-74. DOI：

摘要

提出了一种基于量化和关系的奇异值分解水印算法。算法先对载体图像进行互不重叠的8×8分块;其次对选取的分块进行1级离散小波变换和分别对生成的低频子带和高频子带进行奇异值分解;最后对低频和高频子带奇异值分解后的最大奇异值分别采用量化和关系的嵌入方法嵌入预处理后的水印。实验表明，该算法具有较好的透明性和鲁棒性。与其他算法相比，对于大多数的攻击，该算法具有更好的性能。

Abstract

A novel watermarking algorithm based on quantization and relationship in singular value decomposition domain is proposed.Firstly

cover image is subdivided to non -overlapping blocks of size 8×8.Secondly

perform 1-level discrete wavelet transform for each selected blocks

and do singular value decomposition on the low and high frequency sub -band

respectively.Finally

the biggest singular value of low and high frequency sub -band are modified by exploiting quantification and relationship method to embed preprocessed watermarking image

respectively. Experimental results show that the proposed algorithm has good transparency and robustness. Compared with other algorithms

it has better performance for most attacks.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

A Watermarking Method in Wavelet Domain Optimized by Firefly Algorithm

Microservices anomaly detection based on the correlation of monitoring metrics

Lightweight countermeasures against Spectre attacks on the branch prediction unit

Steganography model guided by attention mechanism and improved dense convolution

A review of large model development: technological evolution from BERT to Deepseek

Related Author

WANG Shi-hui

WANG Yi-ming

WU Shu-qin

TIAN Bei

Related Institution

Beijing Key Laboratory of Digital Printing Machinery, Beijing Institute of Graphic Communication

AI问答

Postal code：100079
Tel：（010）53879206 Email：tmw@bjxintong.com.cn
Technical support is provided by Beijing Founder electronics co., LTD 京ICP备09082226号-64 京公网安备11010602201714号
It is recommended to read the content of this site in Chrome&IE9+. Please switch to extreme mode in browser 360.
Cookies We use cookies to help provide and enhance our service and tailor content. By continuing, you agree to the use of cookies.

⁰