Efficient Robust Watermarking Based on Quaternion Singular Value Decomposition and Coefficient Pair Selection

TL;DR

This paper introduces an improved quaternion singular value decomposition (QSVD) based watermarking scheme that enhances robustness and visual quality by adaptive embedding and coefficient pair selection, outperforming conventional methods.

Contribution

The paper proposes an efficient, algebraic structure-preserving QSVD watermarking method with adaptive embedding and coefficient selection, reducing complexity and improving robustness and image quality.

Findings

Outperforms conventional QSVD in robustness and visual quality.

Resists common geometric attacks effectively.

Demonstrates superiority over other state-of-the-art watermarking methods.

Abstract

Quaternion singular value decomposition (QSVD) is a robust technique of digital watermarking which can extract high quality watermarks from watermarked images with low distortion. In this paper, QSVD technique is further investigated and an efficient robust watermarking scheme is proposed. The improved algebraic structure-preserving method is proposed to handle the problem of "explosion of complexity" occurred in the conventional QSVD design. Secret information is transmitted blindly by incorporating in QSVD two new strategies, namely, coefficient pair selection and adaptive embedding. Unlike conventional QSVD which embeds watermarks in a single imaginary unit, we propose to adaptively embed the watermark into the optimal hiding position using the Normalized Cross-Correlation (NC) method. This avoids the selection of coefficient pair with less correlation, and thus, it reduces embedding impact by decreasing the maximum modification of coefficient values. In this way, compared with conventional QSVD, the proposed watermarking strategy avoids more modifications to a single color image layer and a better visual quality of the watermarked image is observed. Meanwhile, adaptive QSVD resists some common geometric attacks, and it improves the robustness of conventional QSVD. With these improvements, our method outperforms conventional QSVD. Its superiority over other state-of-the-art methods is also demonstrated experimentally.