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Image watermarking techniques have continuously evolved to address newchallenges and incorporate advanced features. The advent of data-drivenapproaches has enabled the processing and analysis of large volumes of data,extracting valuable insights and patterns. In this paper, we propose twocontent-aware quantization index modulation (QIM) algorithms: Content-Aware QIM(CA-QIM) and Content-Aware Minimum Distortion QIM (CAMD-QIM). These algorithmsaim to improve the embedding distortion of QIM-based watermarking schemes byconsidering the statistics of the cover signal vectors and messages. CA-QIMintroduces a canonical labeling approach, where the closest coset to each covervector is determined during the embedding process. An adjacency matrix isconstructed to capture the relationships between the cover vectors andmessages. CAMD-QIM extends the concept of minimum distortion (MD) principle tocontent-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 inreducing embedding distortion compared to traditional QIM. The combination ofcanonical labeling and the minimum distortion principle proves to be powerful,minimizing the need for changes to most cover vectors/carriers. Thesecontent-aware QIM algorithms provide improved performance and robustness forwatermarking applications.

Content-Aware Quantization Index Modulation:Leveraging Data Statistics for Enhanced Image Watermarking