• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.08a
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• pp.253-256
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• 2000

In this paper, we propose the lossless image compression algorithm using the integer wavelet transform. Recently, the S＋P transform is widely used and computed with only integer addition and bit-shift operations, but not proper to remove the correlation of smooth images. then we compare the Harr wavelet of the S＋P transform with various integer coefficient filter banks and apply 9/7 ICFB to the wavelet transform. In addition, we propose a entropy-coding method that exploits the multiresolution structure and the feedback of the prediction error, and can efficiently compress the transformed image for progressive transmission. Simulation results are compared to the compression ratio using the S+P transform with different types of images.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.1
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• pp.19-27
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• 2001

The conventional fractal image compression based on wavelet transform has the disadvantage that the encoding takes many time, since it finds the optimum domain for all the range blocks. In this paper, we propose the fractal image compression based on wavelet transform using the SAS(Self Affine System) techniques. It consists of the range and domain blocks in the wavelet transform, and the range blocks select the domain which is located the relatively same position. In the encoding process, the proposed methods introduce SAS techniques that the searching process of the domains blocks is not required. Therefore, it can perform a fast encoding by reducing the computational complexity. And, the image quality is improved using the different scale factors for each level and the sub-tree in the decoding. As a result, the image quality and the compression ratio are adjustable by the scale factors.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.181.2-181
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• 2001

Stereo PACS (Picture Archiving and Communication System) is not available yet because of some limitations of medical stereo image software and viewing devices. As a stereo PACS viewer, we designed two functions. One is selecting and viewing a multiplexed stereo image directly, and the other is selecting a stereo pair image (left and right sides both) and merging the stereo pair image into a multiplexed image in software. For the medical image compression of 3-D stereo endoscopic images, we used JPEG and Wavelet compression and to determine an acceptable compression rate using PSNR (Peak Signal-to-Noise Ratio). As a result, we got the conclusion that medically acceptable image compression rate should have the PSNR of above about 40[dB] (JPEG (5:1), Wavelet (10:1)).

• Proceedings of the KSRS Conference
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• 1998.09a
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• pp.565-568
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• 1998

In this paper, we propose a new method for multispectral image compression that is based on highly correlated relational properly taken from a spatial image and its wavelet transform. The highly active regions, such as edges or contour, in the spatial domain are appeared as significant coefficients in the wavelet transform domain; and the low active regions like background as insignificant. These characteristics play an important role in designing the system. The simulation results have shown us that the proposed method has better performance in terms of the reconstructed image quality and the transmitted bit rakes. Practically, our system can be successfully applied to the application areas that require of progressive transmission. For some multispectral images with relatively low activity, we have obtained the more good results.

• Journal of the Korean Mathematical Society
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• v.51 no.5
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• pp.941-953
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• 2014

In this paper, a 2-circular matrix theory is developed, and a concept of spectral radius for biorthogonal wavelet is introduced. We propose a novel design method by minimizing the spectral radius and obtain a wavelet which has better performance than the famous 9-7 wavelet in terms of image compression coding.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.04a
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• pp.255-259
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• 2003

본 연구에서는 고해상도 위성 영상에 관한 압축을 연구하였다. 위성영상은 많은 픽셀 정보와 이루어져 있기 때문에 빠른 영상처리와 데이터 보관을 위해서 압축이 필수적이다. 특히 영상압축시 도로정보와 건물, 산림, 지형 등의 특징을 왜곡을 최소화하여 압축하여야 한다. 따라서는 본 연구에서는 함수공간에서 영상 압축이 가능한 웨이브렛을 기반하여 위성 영상의 압축기법을 제안하였으며, 일반적인 정지영상 압축 기법인 JPEG과의 압축성능을 분석하였다. 그 결과 웨이브렛 압축기법이 JPEG보다 1/10 이상의 압축 성능을 보였다.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.505-508
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• 2005

Wavelet scheme can be applied to the various remote sensing problems: conventional multi-resolution image analysis, compression of large image sets, fusion of heterogeneous sensor image and segmentation of features. In this study, we attempted wavelet-based filtering and its analysis. Traditionally, statistical methods and adaptive filter are used to manipulate noises in the image processing procedure. While we tried to filter random noise from optical image and radar image using Discrete Wavelet Transform (DW1) and Stationary Wavelet Transform (SW1) and compared with existing methods such as median filter and adaptive filter. In result, SWT preserved boundaries and reduced noises most effectively. If appropriate thresholds are used, wavelet filtering will be applied to detect road boundaries, buildings, cars and other complex features from high-resolution imagery in an urban environment as well as noise filtering

• Proceedings of the KOSOMBE Conference
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• v.1994 no.12
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• pp.31-38
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• 1994

This paper proposed signal decomposition and multiresolution representation through wavelet transform using wavelet orthonormal basis. And it suggested most appropriate filter for scaling function in multiresoltion representation and compared two compression method, arithmetic coding and Huffman coding. Results are as follows 1. Daub18 coefficient is most appropriate in computing time, energy compaction, image quality. 2. In case of image browsing that should be small in size and good for recognition, it is reasonable to decompose to 3 scale using pyramidal algorithm. 3. For the case of progressive transmittion where requires most grateful image reconstruction from least number of sampls or reconstruction at any target rate, I embedded the data in order of significance after scaling to 5 step. 4. Medical images such as information loss is fatal have to be compressed by lossless method. As a result from compressing 5 scaled data through arithmetic coding and Huffman coding, I obtained that arithmetic coding is better than huffman coding in processing time and compression ratio. And in case of arithmetic coding I could compress to 38% to original image data.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.1B
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• pp.120-127
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• 2000

In this paper, we propose multispectral image compression using classified interband prediction and vector quantization in wavelet domain. This method classifies each region considering reflection characteristics of each band in image data. In wavelet domain, we perform the classified intraband VQ to remove intraband redundancy for a reference band image that has the lowest spatial variance and the best correlation with other band. And in wavelet domain, we perform the classifled interband prediction to remove interband redundancy for the remaining bands. Then error wavelet coefficients between original image and predicted image are intraband vector quantized to reduce prediction error. Experiments on remotely sensed satellite image show that coding efficiency of theproposed method is better than that of the conventional method.

• Journal of Biomedical Engineering Research
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• v.19 no.3
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• pp.305-312
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• 1998

A small PACS based on PC is designed for CR. To receive the digital image from CR, a DICOM Interface Unit (DIU) is designed that complied with the medical image standard, DICOM V3.0. The CR images acquired through the DIU are stored in a file-server; the patient information of the images is stored in a database. To improve the performance of PC and to use it easily, multiresolution images are constructed by wavelet transform and displayed progressively. Wavelet compression method is newly adopted to store the images hierarchically to storage units. In this compression method, the image is decomposed into subclasses of image by wavelet transform, and then the subclasses of the image are vector quantized using a multiresolution codebook. The storage units for CR images were divided into the short-term storage in file-server and the harddisk in viewing station. Image processing tools supported by general PACS is implemented based on PC.