A new QIM-based image watermarking method and system

Embedding capacity, distortion and resilience to attacks are three key indicators to the performance of any image watermarking systems. Study has shown that Quantization Index Modulation (QIM) can in general achieve higher embedding capacity than Spread Spectrum (SS) systems at the same level of distortion. However, QIM is more sensitive to simple attacks such as Additive White Gaussian Noise (AWGN), Uniform Distribution Noise (UDN), and JPEG compression. This paper proposed a system to decompose the host signal into the embedding signal that is designed to be resilient to attacks and the correction signal. QIM is applied on the embedding signal for improving robustness against attacks. A simple yet very effective implementation of the system, Local Average-based QIM (LAQIM) for image watermarking is developed. Theoretical analysis and experimental results have shown that LAQIM substantially reduce the error decoding rate against AWGN, UDN and JPEG compression at no extra cost of embedding distortion in comparison to conventional QIM.