  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.
