• 대한전자공학회논문지
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• 제26권6호
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• pp.89-96
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• 1989

본 논문에서는 영역화에 기초를 둔 영상 부호화의 한 부분인 영역 부호화의 개선에 관한 연구가 수행되었다. 영역화시 texture의 효율적인 표현을 위하여 영상을 stochastic random field로 묘사 될 수 있는 stochastic 영역과 non-stochastic 영역으로 구분한다. 영역 부호화 및 복원시 stochastic 영역에 대해서는 autoregressive model을 이용하고 non-stochastic영역은 2차원 다항식 근사화를 이용한다. 제안 방식은 2차원 다항식 근사화만을 이용한 기존 방식보다 더 좋은 주관적 화질을 가지며, 상대적인 data 감축할 수 있었고 영상의 부호화 및 복원에 필요한 수행시간을 단축시켰다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1511-1514
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• 2002

This paper presents content adaptive image watermark embedding using stochastic visual model based on multiwavelet transform. To embedding watermark, the original image is decomposed into 4 levels using a discrete multiwavelet transform, then a watermark is embedded into the JND(just noticeable differences) of the image each subband. The perceptual model is applied with a stochastic approach fer watermark embedding. This is based on the computation of a NVF(noise visibility function) that have local image properties. The perceptual model with content adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the JND. This method uses stationary Generalized Gaussian model characteristic because watermark has noise properties. The experiment results of simulation of the proposed watermark embedding method using stochastic visual model based on multiwavelet transform techniques was found to be excellent invisibility and robustness.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.172-175
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• 2002

This paper presents perceptual model with a stochastic rnultiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embed at the texture and edge region for more strongly embedded watermark by the SSQ(successive subband quantization). The watermark embedding is based on the computation of a NVF(noise visibility function) that have local image properties. This method uses non-stationary Gaussian model stationary Generalized Gaussian model because watermark has noise properties. In order to determine the optimal NVF, we consider the watermark as noise. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model use the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark benchmark test.

• 한국멀티미디어학회논문지
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• 제6권7호
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• pp.1149-1158
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• 2003

본 논문에서는 멀티웨이브릿 변환영역에서 연속부대역 양자화 및 지각 모델을 이용한 내용기반 적응적 워터마킹 기법을 제안한다. 제안한 방법의 워터마크는 멀티웨이브릿을 통해 분해된 계수들 중 지각적 중요계수(perceptually significant coefficients, PSCs)에 삽입된다. 고주파 부대역에서의 PSC는 연속부대역양자화(successive subband quantization, SSQ)에 의해 결정된다. 문턱값은 각 부대역내의 최대계수의 절반에서 결정된다. 지각모델은 워터마크 삽입을 위한 국부적 영상 특성을 가지는 NVF (noise visibility function)에 기반한 통계적 방법을 적용한다. 이 모델은 워터마크가 노이즈특성을 가지므로 정상상태 일반화 가우스모델을 사용한다. 또한 워터마크는 각 부대역 영역의 분산과 형상계수 (shape parameter)에 의해 추정함으로써 평탄영역과 에지나 텍스쳐 영역에 따라 내용 기반 적응적 척도를 얻는다. 제안한 멀티웨이브릿 변환 기반에서의 워터마크 삽입 방법에 대한 실험 결과 우수한 강인성과 비가시성을 확인하였다.

• 한국멀티미디어학회논문지
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• 제8권6호
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• pp.783-789
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• 2005

This paper is proposed a watermarking technique for copyright protection of multimedia contents. We proposed adaptive watermark detection algorithm using stochastic perceptual model and statistical decision method in DMWT(discrete multi wavelet transform) domain. The stochastic perceptual model calculates NVF(noise visibility function) based on statistical characteristic in the DMWT. Watermark detection algorithm used the likelihood ratio depend on Bayes' decision theory by reliable detection measure and Neyman-Pearson criterion. To reduce visual artifact of image, in this paper, adaptively decide the embedding number of watermark based on DMWT, and then the watermark embedding strength differently at edge and texture region and flat region embedded when watermark embedding minimize distortion of image. In experiment results, the proposed statistical decision method based on multiwavelet domain could decide watermark detection.

• 한국멀티미디어학회논문지
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• 제6권3호
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• pp.508-517
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• 2003

본 논문에서는 쌍직교 웨이브릿 영역에서 워터마크를 삽입할 수 있는 연속 부대역 양자화 및 스토케스틱 다해상도 특성을 갖는 지각 모델을 제안한다. 적응 워터마킹 알고리즘을 갖는 지각모델은 보다 강인한 워터마크 은닉을 위한 방법으로 연속 부대역 양자화(successive subband quantization: SSQ)에 의해서 텍스쳐 및 에지 영역에 삽입한다. 워터마크 삽입은 국부 영상 특성을 갖는 NVF(noise visibility function)함수에 의해 계산된다. 이 방법은 워터마크가 노이즈 특성을 갖기 때문에 영상의 통계적 특성에 기초한 비정상상태(non-stationary state) 가우스 모델과 정상상태(stationary state) 일반화 가우스(generalized Gaussian: GG)모델을 이용한다. 정상상태 GG모델의 삽입은 다해상도 내의 각 부대역별 분산과 형상계수(shape parameter)를 사용한다. 형상계수를 추정하기 위하여 모멘트 정합 방법을 사용한다. 비정상상태 가우스 모델은 각 부대역의 국부 평균 및 분산을 이용한다. 실험결과 우수한 비가시성과 강인성을 확인하였으며, 공격에 대한 실험으로 Stirmark 3.1 benchmark test를 수행하였다.

• 전자공학회논문지B
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• 제29B권9호
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• pp.37-49
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• 1992

The standard definition of computational vision is a set of inverse problems of recovering surfaces from images. Thus the common characteristics of the most early vision problems are ill-posed. The main idea for solving ill-posed problems is to restrict the class of admissible solutions by introducing suitable a priori knowledge. Standard regurarization methods lead to satisfactory solutions of early vision problems but cannot deal effectively and directly with a few general problems, such as discontinuity and fusion of information from multiple modules. In this paper, we discuss limitations of standard regularization theory and present new stochastic method. We will outline a rigorous approach to overcome part of ill-posedness of image restoration, edge detection, and stereo vision problems, based on Bayes estimation and MRF(Markov random field) model, that effectively deals with the problems. This result makes one hope that this framework could be useful in the solution of other vision problems.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 심포지엄 논문집 정보 및 제어부문
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• pp.35-37
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• 2007

Super resolution using stochastic approach which based on the Bayesian approach is to easy modeling for a priori knowledge. Generally, the Bayesian estimation is used when the posterior probability density function of the original image can be established. In this paper, we introduced the improved MAP algorithm based on Bayesian which is stochastic approach in spatial domain. And we presented the observation model between the HR images and LR images applied with MAP reconstruction method which is one of the major in the SR grid construction. Its test results, which are operation speed, chip size and output high resolution image Quality. are significantly improved.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.121-124
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• 2002

This paper presents an adaptive digital image watermarking scheme that uses successive subband quantization (SSQ) and perceptual modeling. Our approach performs a multiwavelet transform to determine the local image properties optimal and the watermark embedding location. The multiwavelet used in this paper is the DGHM multiwavelet with approximation order 2 to reduce artifacts in the reconstructed image. A watermark is embedded into the perceptually significant coefficients (PSC) of the image in each subband. The PSCs in high frequency subbands are selected by setting the thresholds to one half of the largest coefficient in each subband. After the PSCs in each subband are selected, a perceptual model is combined with a stochastic approach based on the noise visibility function to produce the final watermark.

• International Journal of Aeronautical and Space Sciences
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• 제10권1호
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• pp.83-90
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• 2009

A new IRF (Impulse Response Function) analysis technique in high resolution SAR image is presented by taking into account the real clutter environment. In order to investigate the realistic effect of clutter background on the impulse response function of SAR image, an ideally generated impulse response function is superimposed with a large number of background clutter data which are extracted from the various regions of an actual SAR image. As a performance measure, PSLR (Peak Sidelobe Ratio) of the clutter-contained IRF is presented in the various groups of clutter background, and finally the results are compared with the stochastic model.