High Capacity Reversible Watermarking for Images Based on Classified Neural Network

Reversible watermarking is a useful technique for some applications requiring high image quality because it can restore what the original images are as well as protect them. In this paper, a high capacity image reversible watermarking is proposed based on classified neural network. According to the variance of surrounding pixel values, all pixel cells are classified as smooth part or rough part. Correspondingly, two neural networks are designed for smooth pixel prediction and rough pixel prediction, respectively. The watermark is embedded in the prediction errors. In addition, a retesting strategy utilizing the parity detection is presented to increase the capacity of the algorithm. Experimental results show that this algorithm can get smaller prediction error and obtain both higher capacity and good visual quality.