Towards perceptual image watermarking with robust texture measurement

Improving the perceptual quality of images is challenging for the robust watermarking method. To address this problem, most existing algorithms proposed to guide the watermark embedding based on just noticeable difference (JND) model, which mainly consists of corresponding contrast masking factor. However, existing metrics are mostly based on the intra-block texture energy, which cannot exactly describe the texture information and meet the needs of watermarking robustness. In this paper, we present a new texture metric, which incorporates the visual saliency based inter-block texture regularity with the robust discrete cosine transform (DCT) coefficients. The JND model is constructed as a modulation factor by using the proposed robust texture metric, which embeds the intra-block energy and inter-block regularity measurement to capture local and global features. Based on the proposed model, we introduce an adaptive quantization step for the watermarking framework. Both quantitative and qualitative results show that our scheme achieves better performance in comparison with several state-of-the-art schemes.