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：[translated abstract in Chinese] 图像水印技术持续演进以应对新挑战并融合高级特性。数据驱动方法的兴起使得大规模数据的处理与分析成为可能，从中提取有价值的见解与模式。本文提出两种内容感知量化索引调制算法：内容感知QIM与内容感知最小失真QIM。这两种算法通过考虑载体信号向量与消息的统计特性，旨在改善基于QIM的水印方案的嵌入失真。CA-QIM引入规范标记方法，在嵌入过程中确定每个载体向量最近的陪集，并通过构造邻接矩阵来捕捉载体向量与消息之间的关联。CAMD-QIM将最小失真原理扩展至内容感知场景，不再将载体量化至晶格点，而是量化至正确解码区域内的邻近点，同时采用规范标记方法以提升性能。仿真结果表明，与传统QIM相比，CA-QIM与CAMD-QIM能有效降低嵌入失真。规范标记与最小失真原理的结合展现出强大优势，极大减少了对大多数载体向量/载波修改的需求。这些内容感知QIM算法为水印应用提供了更优的性能与鲁棒性。