High-performance reversible data hiding scheme via block dynamic selection

像素 嵌入 信息隐藏 计算机科学 块(置换群论) 算法 图像(数学) 计算机视觉 方案(数学) 模式识别(心理学) 块大小 计算复杂性理论 人工智能 数学 数字水印 图像纹理 图像处理 隐写术
作者
Zhengwei Zhang,Weien Xiao,Fenfen Li
出处
期刊:Array [Elsevier BV]
卷期号:28: 100618-100618
标识
DOI:10.1016/j.array.2025.100618
摘要

Pixel value ordering (PVO) is a widely used reversible data hiding (RDH) technique that leverages pixel correlations within image blocks to generate high-fidelity stego-images. However, its embedding performance is limited by fixed block sizes, which fail to adapt to varying texture complexities. To address this issue, we propose a novel RDH method based on block dynamic selection. First, we employ a 2 × 3 image block as the basic embedding unit. In addition, we introduce a dual-layer embedding mechanism that partitions the cover image into checkerboard-like gray and white blocks, which enables the use of neighboring pixels to more accurately estimate the complexity of each block. For flat blocks with lower complexity values, we further subdivide the 2 × 3 block into two 1 × 3 sub-blocks, and a pixel-based pre-ordering scheme is proposed to determine the optimal ordering of pixels within the block, thereby increasing the number of expandable errors. For texture blocks, we utilize the adaptive pixel distribution density (APDD) to select the most suitable neighboring block for merging. By leveraging location information from two predicted pixels in the current block, APDD dynamically selects the optimal block, effectively enhancing its embedding potential. Experimental results demonstrate that the proposed method achieves a PSNR improvement of up to 1.46 dB compared to state-of-the-art methods under the same embedding capacity. • Proposes a block dynamic selection mechanism to adaptively determine embedding unit size. • Introduces a pixel pre-sorting scheme based on neighbor means to increase expandable errors. • Employs APDD using predicted pixel locations to guide adaptive merging of texture blocks. • Achieves significantly higher PSNR, with gains up to 1.46 dB over state-of-the-art methods.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
bsmark发布了新的文献求助10
刚刚
刚刚
wuxunxun2015完成签到,获得积分10
刚刚
molihuakai应助季末默相依采纳,获得10
1秒前
1秒前
littlequiet发布了新的文献求助10
1秒前
breeder完成签到,获得积分10
1秒前
CKK完成签到,获得积分10
2秒前
拼搏的秋柔完成签到,获得积分10
2秒前
张欢馨应助可爱初瑶采纳,获得10
2秒前
无情的羊青完成签到,获得积分10
2秒前
长情代玉完成签到,获得积分10
2秒前
Lilly完成签到,获得积分10
3秒前
啦啦啦完成签到,获得积分10
3秒前
3秒前
fujuzhang完成签到,获得积分10
3秒前
CKK发布了新的文献求助30
4秒前
Warma完成签到,获得积分10
4秒前
wangbq完成签到 ,获得积分10
4秒前
xuan发布了新的文献求助10
5秒前
冲冲冲完成签到,获得积分10
5秒前
xcc发布了新的文献求助10
5秒前
大胆的马里奥完成签到 ,获得积分10
6秒前
大胆书南完成签到,获得积分10
6秒前
Sam完成签到,获得积分10
7秒前
不吃橘子完成签到,获得积分10
7秒前
快乐百分百完成签到,获得积分10
7秒前
7秒前
8秒前
oooo发布了新的文献求助10
8秒前
博修发布了新的文献求助10
8秒前
爱撒娇的皓轩完成签到,获得积分10
9秒前
9秒前
牧青应助Splaink采纳,获得30
9秒前
SciGPT应助曜曜采纳,获得10
9秒前
高兴灵薇发布了新的文献求助10
9秒前
蛋蛋发布了新的文献求助20
10秒前
Orange应助萧一江采纳,获得10
10秒前
11秒前
着急的大米完成签到 ,获得积分10
11秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Römisch-Germanische Forschungen 1000
APA handbook of comparative psychology: Basic concepts, methods, neural substrate, and behavior 1000
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
An introduction of AMSTAR-2: a quality assessment instrument of systematic reviews including randomized or non-randomized controlled trials or both 500
An introduction to a measurement tool to assess the methodological quality of systematic reviews/meta-analysis: AMSTAR 500
The formulation methods and steps of umbrella review 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7606408
求助须知:如何正确求助?哪些是违规求助? 9182203
关于积分的说明 19665622
捐赠科研通 7180610
什么是DOI,文献DOI怎么找? 3269573
关于科研通互助平台的介绍 2433514
邀请新用户注册赠送积分活动 2263793