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]
日期:2025-11-28
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.
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