基于预测误差扩展的高阶像素可逆水印方案

Bharathi Chidirala, Bibhudendra Acharya
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引用次数: 0

摘要

在数字内容充斥、在线图像泛滥的当今数字驱动世界,数字图像水印至关重要。它能确保版权保护,维护内容的真实性,并保持视觉信息的完整性。本文提出了一种基于灰度图像像素强度值分割和传统预测误差扩展的可逆水印技术。首先,图像的像素值被分割成两个不同的组。百位和十位上的值被归类为高阶像素组(\(\alpha\)),而单位位上的值被归类为低阶像素组(\(\beta\))。通过这种分割过程,我们发现许多相邻像素共享相同的值。为了嵌入数据,传统的预测误差扩展技术被专门用于高阶像素组。在确定预测误差后,将对其进行扩展,并将秘密数据嵌入其中。经实验验证,该方案优于现有的几种技术方案。在单个 512 (\times\) 512 图像中嵌入约 65335 比特的高容量,水印图像的 PSNR 约为 36.34 dB,SSIM 约为 0.94。测试了 10,000 和 20,000 比特的有效载荷,其 PSNR 值分别为 44.56 dB 和 41.35 dB。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Prediction error expansion based reversible watermarking scheme in higher order pixels

Prediction error expansion based reversible watermarking scheme in higher order pixels

In today’s digitally-driven world flooded with digital content and the widespread availability of images online, digital image watermarking is crucial. It ensures copyright protection, maintains content authenticity, and preserves the integrity of visual information. In this paper, a reversible watermarking technique is proposed based on splitting the gray scale image pixel’s intensity value and conventional prediction error expansion. To begin with, the pixel values of an image are split and posted into two distinct groups. The values in the hundred’s and ten’s place are categorized as the higher order pixel group (\(\alpha\)), while the values in the units place are categorized as lower order pixel group (\(\beta\)). Through this splitting process, it has been noticed that numerous adjacent pixels share identical values. In order to embed data, the conventional Prediction Error Expansion technique is employed specifically for the higher order pixel group. After determining the prediction error, it will be expanded and secret data is embedded into it. This scheme has been experimentally verified that it is superior than few existing state of art schemes. With a high embedding capacity of about 65335 bits in a single 512 \(\times\) 512 image, the watermarked images exhibited a PSNR of approximately 36.34 dB and an SSIM of around 0.94. Payloads for 10,000 and 20,000 bits are tested and their PSNR values are 44.56 dB and 41.35 dB respectively.

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