Image watermarking techniques have continuously evolved to address new challenges and incorporate advanced features. The advent of data-driven approaches has enabled the processing and analysis of large volumes of data, extracting valuable insights and patterns. In this paper, we propose two content-aware quantization index modulation (QIM) algorithms: Content-Aware QIM (CA-QIM) and Content-Aware Minimum Distortion QIM (CAMD-QIM). These algorithms aim to improve the embedding distortion of QIM-based watermarking schemes by considering the statistics of the cover signal vectors and messages. CA-QIM introduces a canonical labeling approach, where the closest coset to each cover vector is determined during the embedding process. An adjacency matrix is constructed to capture the relationships between the cover vectors and messages. CAMD-QIM extends the concept of minimum distortion (MD) principle to content-aware QIM. Instead of quantizing the carriers to lattice points, CAMD-QIM quantizes them to close points in the correct decoding region. Canonical labeling is also employed in CAMD-QIM to enhance its performance. Simulation results demonstrate the effectiveness of CA-QIM and CAMD-QIM in reducing embedding distortion compared to traditional QIM. The combination of canonical labeling and the minimum distortion principle proves to be powerful, minimizing the need for changes to most cover vectors/carriers. These content-aware QIM algorithms provide improved performance and robustness for watermarking applications.

翻译：图像水印技术不断发展，以应对新挑战并融合先进功能。数据驱动方法的出现使得大规模数据的处理与分析成为可能，从而提取有价值的见解和模式。本文提出了两种内容感知量化索引调制（QIM）算法：内容感知QIM（CA-QIM）和内容感知最小失真QIM（CAMD-QIM）。这些算法通过考虑载体信号向量和消息的统计数据，旨在改善基于QIM的水印方案的嵌入失真。CA-QIM引入了一种规范标记方法，在嵌入过程中确定每个载体向量最近的陪集。构建邻接矩阵以捕捉载体向量与消息之间的关系。CAMD-QIM将最小失真原则的概念扩展到内容感知QIM。CAMD-QIM不是将载波量化到格点，而是将其量化到正确解码区域内的邻近点。CAMD-QIM还采用规范标记以增强其性能。仿真结果表明，与传统QIM相比，CA-QIM和CAMD-QIM在降低嵌入失真方面具有有效性。规范标记与最小失真原则的结合被证明是强大的，最大限度地减少了对大多数载体向量/载波的修改需求。这些内容感知QIM算法为水印应用提供了改进的性能和鲁棒性。