Pyramid-based texture analysis/synthesis
SIGGRAPH '95 Proceedings of the 22nd annual conference on Computer graphics and interactive techniques
Multiresolution sampling procedure for analysis and synthesis of texture images
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
Filters, Random Fields and Maximum Entropy (FRAME): Towards a Unified Theory for Texture Modeling
International Journal of Computer Vision
Mixtures of probabilistic principal component analyzers
Neural Computation
A Parametric Texture Model Based on Joint Statistics of Complex Wavelet Coefficients
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: modeling, learning, sampling and computing, Part I
Parallel controllable texture synthesis
ACM SIGGRAPH 2005 Papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information-theoretic analysis of information hiding
IEEE Transactions on Information Theory
Attacks on digital watermarks: classification, estimation based attacks, and benchmarks
IEEE Communications Magazine
Secure spread spectrum watermarking for multimedia
IEEE Transactions on Image Processing
Motion-compensated residue preprocessing in video coding based on just-noticeable-distortion profile
IEEE Transactions on Circuits and Systems for Video Technology
Spread-spectrum watermark by synthesizing texture
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
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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.