High Capacity Watermarking in Nonedge Texture Under Statistical Distortion Constraint

Abstract

High-capacity image watermarking scheme aims at maximize bit rate of hiding information, neither eliciting perceptible image distortion nor facilitating special watermark attack. Texture, in preattentive vision, delivers itself by concise high-order statistics, and holds high capacity for watermark. However, traditional distortion constraint, e.g. just-noticeable-distortion (JND), cannot evaluate texture distortion in visual perception and thus imposes too strict constraint. Inspired by recent work of image representation [9], which suggests texture extraction and mix probability principal component analysis for learning texture feature, we propose a distortion measure in the subspace spanned by texture principal components, and an adaptive distortion constraint depending on image local roughness. The proposed spread-spectrum watermarking scheme generates watermarked images with larger SNR than JND-based schemes at the same distortion level allowed, and its watermark has a power spectrum approximately directly proportional to the host image’s and thereby more robust against Wiener filtering attack.