Título: AN EMINENT REVERSIBLE DATA HIDING SYSTEM WITH EDGE PREDICTION AND VARIATION EXPANSION
Autores: Deepthi, Satharla Raga; M.Tech Student, Dept of ECE, St. Johns College of Engineering and Technology, Yerrakota, Yemmiganur, Kurnool(Dist), A.P
Devanna, H.; Associate Professor, Dept of ECE, St. Johns College of Engineering and Technology, Yerrakota, Yemmiganur, Kurnool(Dist), A.P
Sudhakar, K.; Associate Professor, Dept of ECE, St. Johns College of Engineering and Technology, Yerrakota, Yemmiganur, Kurnool(Dist), A.P
Fecha: 2013-06-02
Publicador: International journal of computer and electronics research
Fuente:
Tipo: Peer-reviewed Article
Tema: Median edge detector; Gradient-adjusted Predictor; Reversible watermarking; prediction-error expansion
Descripción: The optimized watermarking transform come into view to commence extremely low distortion which outperforms the fundamental Difference expansion transform of Tian and the traditional median edge detector and gradient-adjusted Predictor error extension transforms. The median edge detector predictor is used in JPEG-LS, as a substitute of the simple difference between adjacent pixels. The gradient-adjusted Predictor used in context-based adaptive lossless image coding algorithm appeared to provide better results than the median edge detector predictor in watermarking schemes. Uncomplicated linear predictors are considered as an alternative for looking an elevated-complication predictor and optimize the data embedding procedure. Extremely low distortion renovate for prediction-error expansion reversible watermarking has been predictable. The Proposed technique outperforms a representative previous art of Difference expansion change, and it is shown that Tian’s transform is equal to a prediction-error expansion of a simple linear predictor with enhanced embedding. Sensible reversible watermarking algorithms based on the proposed transform have been considered. The suitable application areas comprise low bit-rate image annotation for captioning and cataloging
Idioma: Inglés

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