• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.3
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• pp.347-352
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• 2011

Entropy used for measuring the richness in details of the image and absolute mean brightness error(AMBE) providing a change in the image global appearance are two quantitative measures generally used for measuring quality of images. In this paper, we propose an entropy and AMBE-based thresholding method to binalize a given image. The effectiveness of the proposed method is demonstrated by thresholding experiments on nine test images and comparison with other conventional thresholding methods, that is, Otsu method and entropy-based method.

• Journal of Broadcast Engineering
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• v.19 no.4
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• pp.453-467
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• 2014

A typical histogram equalization contrast enhancement effect for improving the image quality is excellent. However, because it appears that excessive changes of the brightness values, The average brightness of the image is changing in units of frames of applications such as a TV video is unsuitable. In order to solve these drawbacks, a modified method of histogram equalization on various studies have been made. But the result images of existing methods sometimes shown visual degradations such as over-enhancement and false contouring. In this paper, we propose improved contrast enhancement method through bi-histogram equalization using target mean brightness based on differential compression method. The proposed method is based on the average brightness value by dividing the histogram, the histogram for each zone, according to the frequency differential of compression. And equalize the modified histogram based on target mean brightness. This allows to suppress deterioration of picture quality, and changes in the average brightness of each frame of video, while maintaining and improving the contrast. Experimental results show that the proposed method compared to the conventional method, the average brightness of each frame from a movie well maintained, and no degradation of the image quality showed a good effect to improve the contrast.

• Journal of Internet Computing and Services
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• v.13 no.3
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• pp.61-73
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• 2012

This paper proposes a histogram specification based image enhancement method, which uses the brightness mean and variance of an image to maximize the entropy of the image. In our histogram specification step, the Gaussian distribution is used to fit the input histogram as well as produce the target histogram. Specifically, the input histogram is fitted with the Gaussian distribution whose mean and variance are equal to the brightness mean(${\mu}$) and variance(${\sigma}2$) of the input image, respectively; and the target Gaussian distribution also has the mean of the value ${\mu}$, but takes as the variance the value which is determined such that the output image has the maximum entropy. Experimental results show that compared to the existing methods, the proposed method preserves the mean brightness well and generates more natural looking images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.59-62
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• 2017

본 논문에서는 평균 밝기 부분에 가중치 맵으로써 감마 변환에 기반한 선형 결합을 제안하고자 한다. 기존의 패치를 기반으로 한 가중치 맵은 평균 밝기 부분에서 영상 내 밝기 값이 한쪽으로 치우쳐 영상의 밝은 부분이 과포화 상태가 되어 세부 정보가 손실되는 단점이 있다. 이에 본 논문에서는 전역적 및 지역적 영상의 평균 밝기 값을 이용하여 감마 변환된 값을 선형 결합 시켜줌으로써 영역 내 세부 정보를 보존시키고 주관적 화질을 향상시켰다. 실험을 통해 결과를 분석하고 성능을 비교하여 기존 알고리듬에 비해 제안한 알고리듬이 우수함을 증명하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1998.06a
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• pp.193-196
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• 1998

본 논문에서는 동영상 부호화에 있어서의 영상 간 중복성을 제거하기 위한 수단으로 사용되는 움직임 추정 방식에 관련된 내용으로 특히 밝기가 변하는 영상에 대해 부호화 성능을 높여 주는 움직임 추정방식에 대해 부호화 제안하였다. 제안한 움직임 추정은 기존의 평균 절대 오차나 평균 제곱 오차를 기반으로 하는 기존의 움직임 추정에 의한 움직임 벡터와 매크로블록 간 오차의 최소 분산을 가지는 움직임 벡터 중 부호화 시 적은 비트를 필요로 하는 움직임 벡터를 이용함으로써 일반 영상에는 기존의 방법과 유사한 부호화 성능을 나타내고, 화면의 밝기가 급격하게 변하는 영상에서는 기존의 방법보다 우수한 부호화 성능을 나타내도록 하는 방법이다.

• Journal of Broadcast Engineering
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• v.22 no.6
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• pp.702-712
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• 2017

In this paper, we propose an unsharp masking algorithm using Laplacian as a weight map for the signal structure and a gamma transformation algorithm using image mean intensity as a weight map for mean intensity. The conventional weight map based on the patch has a disadvantage in that the brightness in the image is shifted to one side in the signal structure and the mean intensity region. So the detailed information is lost. In this paper, we improved the detail using unsharp masking of patch unit and proposed linearly combined the gamma transformed values using the average brightness values of the global and local images. Through the proposed algorithm, the detail information such as edges are preserved and the subjective image quality is improved by adjusting the brightness of the light. Experiment results show that the proposed algorithm show better performance than conventional algorithm.

• Korean Journal of Cognitive Science
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• v.10 no.3
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• pp.39-49
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• 1999

The space of IID([ndependently, Identically Distributed) textures was built with axes of mean luminance and contrast, and studied on what kind of mechanisms were required to mediate texture segregation in this space. The conjecture was tested that one of these mechanisms is sensitive to the differences between the means of textures to be discriminated, whereas the other is sensitive to the differences between variances. The probability of discrimination was measured for various pairs of textures in the lID space The data were well fit by a model in which discrimination depends on two mechanisms whose responses are combined by probability summation. The conjecture was rejected that two mechanisms respectively tuned to mean and variance of texture function in segregation. Discrimination within space is mediated by 2 independent channels however: the 2 independent channels are not exactly tuned to texture mean and variance. One m mechanism was primarily sensitive to texture mean, whereas the other was sensitive to b both texture mean and variance.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2002.11a
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• pp.510-513
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• 2002

대부분의 이진화 알고리즘은 임계치를 결정하기 위하여 히스토그램을 사용하여 밝기분포를 분석한다. 배경과 물체의 명도차이가 큰 경우에는 분할을 위해 양봉(bimodal) 히스토그램으로 표현하여 최적의 임계치를 찾기 위해 히스토그램 골짜기(valley)를 선택하는 것만으로도 양호한 임계치 결과를 얻을수 있으나, 배경과 물체의 밝기 차이가 크지 않거나 밝기 분포가 양봉 특성을 보이지 않을 때는 히스토그램 분석만으로 적절한 임계치를 얻기 어렵다. 그리고 한 영상에서는 넓은 영역에 걸쳐 명암도 변화가 일어나고 다양한 유형의 물체가 포함되어 있으므로 스케치 특징점 유무를 판별하는 임계치의 결정에는 애매 모호함이 존재한다. 따라서 본 논문에서는 영상에 대해 삼각형 타입의 소속함수를 적용하여 임계치를 동적으로 설정하고 영상을 이진화하는 방법을 제안한다. 제안된 퍼지 이진화 방법은 평균 밝기 값을 기준으로 가장 어두운 픽셀 값과 가장 밝은 픽셀값의 거리를 계산하여 밝기의 조정률을 구하여 최소 밝기값과 최대 밝기 값을 설정하고 삼각형의 소속 함수에 적용한다. 소속 함수에 적용된 소속도를 a-cut 을 적용하여 영상을 이진화한다. 다양한 영상에 적용한 결과, 기존의 이진화 방법보다 제안된 퍼지 이진화 방법이 효율적인 것을 알 수 있었다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.3
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• pp.721-726
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• 2004

The image binarization is applied frequently as one part of the preprocessing phase for a variety of image processing techniques such as character recognition and image analysis, etc. The performance of binarization algorithms is determined by the selection of threshold value for binarization, and most of the previous binarization algorithms analyze the intensity distribution of the original images by using the histogram and determine the threshold value using the mean value of Intensity or the intensity value corresponding to the valley of the histogram. The previous algorithms could not get the proper threshold value in the case that doesn't show the bimodal characteristic in the intensity histogram or for the case that tries to separate the feature area from the original image. So, this paper proposed the novel algorithm for image binarization, which, first, segments the intensity range of grayscale images to several intervals and calculates mean value of intensity for each interval, and next, repeats the interval integration until getting the final threshold value. The interval integration of two neighborhood intervals calculates the ratio of the distances between mean value and adjacent boundary value of two intervals and determine as the threshold value of the new integrated interval the intensity value that divides the distance between mean values of two intervals according to the ratio. The experiment for performance evaluation of the proposed binarization algorithm showed that the proposed algorithm generates the more effective threshold value than the previous algorithms.

• Journal of the Korean Society of Radiology
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• v.9 no.4
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• pp.227-234
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• 2015

In this paper, we studied possibility about application for CAD on diffuse liver disease through pixel texture analysis parameters(average gray level, skewness, entropy) which based statistical property brightness histogram and image analysis using brightness difference liver and kidney parenchyma. The experiment was set by ROI ($50{\times}50$ pixels) on liver ultrasound images.(non specific, fatty liver, liver cirrhosis) then, evaluated disease recognition rates using 4 types pixel texture analysis parameters and brightness gap liver and kidney parenchyma. As a results, disease recognition rates which contained average brightness, skewness, uniformity, entropy was scored 100%~96%, they were high. In brightness gap between liver and kidney parenchyma, non specific was $-1.129{\pm}12.410$ fatty liver was $33.182{\pm}11.826$, these were shown significantly difference, but liver cirrhosis was $-1.668{\pm}10.081$, that was somewhat small difference with non specific case. Consequently, pixel texture analysis parameter which scored high disease recognition rates and CAD which used brightness difference of parenchyma are very useful for detecting diffuse liver disease as well as these are possible to use clinical technique and minimize reading miss. Also, it helps to suggest correct diagnose and treatment.