• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.6C
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• pp.857-864
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• 2004

Many image compression standards such as JPEG, MPEG or H.263 are based on the discrete cosine transform (DCT) and quantization method. Quantization error. is the major source of image quality degradation. The current dequantization method assumes the uniform distribution of the DCT coefficients. Therefore the dequantization value is the center of each quantization interval. However DCT coefficients are regarded to follow Laplacian probability density function (pdf). The center value of each interval is not optimal in reducing squared error. We use mean of the quantization interval assuming Laplacian pdf, and show the effect of correction on image quality. Also, we compare existing quantization error to corrected quantization error in closed form. The effect of PSNR improvements due to the compensation to the real image is in the range of 0.2 ∼0.4 ㏈. The maximum correction value is 1.66 ㏈.

• Journal of Korean Academy of Oral and Maxillofacial Radiology
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• v.27 no.1
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• pp.7-16
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• 1997

This study was performed to demonstrate the method of image reformation for dental implants, using a personal computer with inexpensive softwares and to compare the images reformatted using the above method with those using Dentascan software. CT axial slices of 5 mandibles of 5 volunteers from GE Highspeed Advantage(GE Medical systems, U.S.A.) were used. Personal computer used for image reformation was PowerWave 6041120 (Power Computing Co, U.S.A.) and softwares used were Osiris (Univ. Hospital of Geneva, Switzerland) and Import ACCESS V1.H Designed Access Co., U.S.A.) for importing CT images and NIH Image 1.58 (NIH, U.S.A.) for image processing. Seven images were selected among the serial reconstructed cross-sectional images produced by Dentascan(DS group). Seven resliced cross-sectional images at the same position were obtained at the personal computer(PC group). Regression analysis of the measurements of PC group was done against those of DS group. Measurements of the bone height and width at the reformed cross-sectional images using Mac-compatible computer were highly correlated with those using workstation with Dentascan software(height : r²=0.999, p<0.001, width : r²=0.991, p<0.001). So, it is considered that we can use a personal computer with inexpensive softwares for the dental implant planning, instead of the expensive software and workstation.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.8
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• pp.47-54
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• 1998

With the increasing popularity of the use of large-volume image database in various application, it becomes imperative to build an efficient and fast retrieval system to browse through the entire database. We present a new method for a content-based image retrieval by using a variable block size and block matching algorithm. Proposed approach is reflecting image features that exploit visual cues such as color and space allocation of image and is getting the fast retrieval time by automatical convergence of retrieval times which adapt to wanting similarity value. We have implemented this technique and tested it for a database of approximately 150 images. The test shows that a 1.9 times fast retrieval time compare to J & V algorithm at the image retrieval efficiency 0.65 and that a 1.83 times fast retrieval time compare to predefined fixed block size.

• Journal of applied mathematics & informatics
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• v.27 no.1_2
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• pp.175-182
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• 2009

Microscopic imaging system often requires the algorithm to adjust location of camera lenses automatically in machine level. An effort to detect the best focal point is naturally interpreted as a mathematical inverse problem [1]. Following Wiener's point of view [2], we interpret the focus level of images as the quantified factor appeared in image degradation model: g = $f{\ast}H+{\eta}$, a standard mathematical model for understanding signal or image degradation process [3]. In this paper we propose a simple, very fast and robust method to compare the degradation parameters among the multiple images given by introducing outlier analysis of histogram.

• Journal of Multimedia Information System
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• v.5 no.1
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• pp.35-42
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• 2018

Direct volume rendering (DVR) is an important 3D visualization method for medical images as it depicts the full volumetric data. However, because DVR renders the whole volume, regions of interests (ROIs) such as a tumor that are embedded within the volume maybe occluded from view. Thus, conventional 2D cross-sectional views are still widely used, while the advantages of the DVR are often neglected. In this study, we propose a new visualization algorithm where we augment the 2D slice of interest (SOI) from an image volume with volumetric information derived from the DVR of the same volume. Our occlusion-based DVR augmentation for SOI (ODAS) uses the occlusion information derived from the voxels in front of the SOI to calculate a depth parameter that controls the amount of DVR visibility which is used to provide 3D spatial cues while not impairing the visibility of the SOI. We outline the capabilities of our ODAS and through a variety of computer tomography (CT) medical image examples, compare it to a conventional fusion of the SOI and the clipped DVR.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.321-322
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• 1998

In this paper, a medical images retrieval method using feature extraction based on wavelet transform is proposed. We used energy of coefficients which is represented by wavelet transform. The proposed retrieval algorithm is comprised of the two retrieval. At first, we make a energy map for wavelet coefficient of a query image and then compare is to one of db image. And then we use an edge information of the query image to retrieve the images selected at the first retrieval once more. Consequently some retrieved images are displayed on screen.

• IEMEK Journal of Embedded Systems and Applications
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• v.15 no.2
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• pp.61-70
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• 2020

Convolutional Neural Networks (CNN) have been used extensively in recent times to solve image classification and segmentation problems. However, the use of CNNs in image super-resolution problems remains largely unexploited. Filter interpolation and prediction model methods are the most commonly used algorithms in super-resolution algorithm implementations. The major limitation in the above named methods is that images become totally blurred and a lot of the edge information are lost. In this paper, we analyze super resolution based on CNN and the wavelet transform super resolution method. We compare and analyze the performance according to the number of layers and the training data of the CNN.

• Journal of the Semiconductor & Display Technology
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• v.8 no.3
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• pp.45-50
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• 2009

Recently, block-based transforms, like discrete cosine transform (DCT), have been widely used in image and video coding standards, but block-based transforms have a weak point with blocking effect. However, the integer lapped orthogonal transform (ILOT) is a tool for block-based coding with bases functions that overlap near blocks, so it has a strong point against blocking effect. Although it has slightly higher arithmetic complexity than the DCT, the coding gain is significantly higher with much less blocking artifacts. This paper introduces the integer lapped orthogonal transforms and discrete cosine transform. And we compare the performance of DCT with ILOT which is proposed a new efficient method for image coding applications.

• Journal of Korean Academy of Nursing
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• v.12 no.2
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• pp.80-90
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• 1982

This study was undertaken to determine the influence of a chronic disease on body image, and to show that body image is directly related to one's self concept. Body image is the concept of one's own body based on present and past perception, and is elated to one's self concept. Body image is a dynamic concept constantly changing throughout the life cycle but it changes greatly in illness, surgery, and accident. The child with a chronic disease experiences pain and immobilization due to illness and he/she experiences a strange environment in the hospital. illness often brings feeling of frustration and loss of self-esteem. Therefore this study was done to compare the body image of a child with a chronic disease（Asthma), with that of a normal healthy child, and to determine the relationship between the body image and self concept. The subjects in this study were 36 children being treated for asthma at the allergy clinic of Y University Hospital in Seoul (patient group) and 44 children attending elementary school in Kwanak Ku Seoul (normal healthy group). For the measurement of the body image, the researcher used Secord & Jourard's Body Cathexis Scale, and another scale which was constructed after reading about Osgood's Semantic Differential Method. For the measurement of the self concept, the researcher used Jacox & Stewart's Health Self Concept. The period for data collection was from October 7th to October 27th, 1982. The analysis of data was done by use of Percentage, t-test, Chi-square test, Pearson Correlation Coefficient and ANOVA, The results of the study were as follows: 1. The first hypothesis,“That the chronically ill (Asthma) child will have a more negative body image than the normal healthy child.”was supported. 2. The second hypothesis,“The more negative the body image, the lower the self concept.”was also supported. 3. The researcher failed to obtain statistically significant results in the analysis of the general characteristics which affect the body image except in the case of the older child as compare to the younger Child having a mole positive body image (r=.2751, r=.2481, p<.05). However it was found that, 1) Boy's have a more positive body image than girls (Mean=〔37.81, 141.09〕,〔37.00, 126.54〕), 2) The child who has been hospitalized has a more negative body image than the child who has never been hospitalized (Mean=〔33.25, 122.45〕,〔35.68, 129.93〕). 3) The younger the child when the disease is discovered and diagnosed, the more negative the body image (Onset of illness: Mean=〔31.44, 117.33〕,〔34.00, 103.50〕, 〔35.75, 140.38〕,〔36.33, 130.00〕, Time of Diagnosis: Mean=〔29.00, 117.33〕,〔33.89, 115.00〕,〔33.36, 124.93〕,〔37.10, 139. 20〕). In conclusion the chronically ill(.Asthma) child has a more negative body image than the normal healthy child, and the more negative the body image the lower the self concept. Therefore the concept of body image is useful in understanding the influences of chronic disease on body' image and self concept.

• The KIPS Transactions:PartB
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• v.11B no.5
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• pp.581-586
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• 2004

In this paper, we proposed a content-based image retrieval using a color feature for central region and its optimized comparing bin method. Human's visual characteristic is influenced by existent of central object. So we supposed that object is centrally located in image and then we extract color feature at central region. When the background of image is simple, the retrieval result can be bad affected by major color of background. Our method overcome this drawback as a result of the human visual characteristic. After we transform Image into HSV color space, we extract color feature from the quantized image with 16 level. The experimental results showed that the method using the eight high rank bin is better than using the 16 bin The case which extracts the feature with image's central region was superior compare with the case which extracts the feature with the whole image about 5%.