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Author: search.filters.author.Pintavirooj, ChuchartSubject: search.filters.subject.Gaussian weight predictor

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    Optimal Gaussian weight predictor and sorting using genetic algorithm for reversible watermarking based on PEE and HS
    (2016-09-01)
    Panyindee, Chaiyaporn
    ;
    Pintavirooj, Chuchart
    This paper introduces a reversible watermarking algorithm that exploits an adaptable predictor and sorting parameter customized for each image and each payload. Our proposed method relies on a well-known prediction-error expansion (PEE) technique. Using small PE values and a harmonious PE sorting parameter greatly decreases image distortion. In order to exploit adaptable tools, Gaussian weight predictor and expanded variance mean (EVM) are used as parameters in this work. A genetic algorithm is also introduced to optimize all parameters and produce the best results possible. Our results show an improvement in image quality when compared with previous conventional works.
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    Optimizations using the genetic algorithm for reversible watermarking
    (2013-09-02)
    Panyindee, Chaiyaporn
    ;
    Pintavirooj, Chuchart
    Important requirements for reversible data hiding techniques: the embedding capacity should be large and distortion should be low. This paper represents a high performance reversible watermarking technique which involves adaptable predictor and sorting parameter to suit each image and each payload in order get lowest image distortion. Our proposed method relies on a well-known prediction error (PE) expansion technique. Having small PE values and a harmonious PE sorting parameter will greatly decrease distortion. In order to get adaptable tools, Gaussian weight predictor and expanded variance mean were used as parameters in this work. A genetic algorithm has also been introduced to optimize all parameters and produce the best results possible. Implementation showed a significantly improved result compared to previous work. © 2013 IEEE.
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    Reversible watermarking using Gaussian weight prediction and genetic algorithm
    (2013-01-01)
    Panyindee, Chaiyaporn
    ;
    Pintavirooj, Chuchart
    This paper represents a high performance reversible watermarking technique which involve adaptable predictor and sorting parameter to suit each image and each payload in order get lowest image distortion. Our proposed method relies on a well-known prediction error (PE) expansion technique. Having small PE values and a harmonious PE sorting parameter will greatly decrease distortion. In order to get adaptable tools, Gaussian weight predictor and expanded variance mean were used as parameters in this work. A genetic algorithm has also been introduced to optimize all parameters and produce the best results possible. Implementation showed a significantly improved result compared to previous work.