• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2000.12a
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• pp.125-128
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• 2000

The conventional fractal decoding was required a vast amount computational complexity. Since every range blocks was implemented to IFS(iterated function system). In order to improve this, it has been suggested to that each range block was classified to iterated and non-iterated regions. If IFS region is contractive, then it can be performed a fast decoding. In this paper, a searched region of the domain in the encoding is limited to the range region that is similar with the domain block, and IFS region is a minimum. So, it can be performed a fast decoding by reducing the computational complexity for IFS in fractal image decoding.

• Journal of the Korea Computer Graphics Society
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• v.9 no.3
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• pp.23-29
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• 2003

There is the temporal correlation of the video sequence between the motion vector of current block and the motion vector of the previous block. If we can obtain useful and enough information from the motion vector of the same coordinate block of the previous frame, the total number of search points used to find the motion vector of the current block may be reduced significantly. In this paper, we propose the block-matching motion estimation using an adaptive initial search point by the predicted motion information from the same block of the previous frame. And the first search point of the proposed algorithm is moved an initial point on the location of being possibility and the searching process after moving the first search point is processed according to the fast search pattern. Simulation results show that PSNR(Peak-to-Signal Noise Ratio) values are improved UP to the 1.05dB as depend on the image sequences and improved about 0.33~0.37dB on an average. Search times are reduced about 29~97% than the other fast search algorithms. Simulation results also show that the performance of the proposed scheme gives better subjective picture quality than the other fast search algorithms and is closer to that of the FS(Full Search) algorithm.

• Journal of Korea Spatial Information System Society
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• v.10 no.2
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• pp.1-12
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• 2008

Automatic semiconductor manufacture system generating transaction from 50,000 to 500,000 per a second needs storage management system processing very large data at once. A lot of storage management systems are researched for storing very large data. Existing storage management system is typical DBMS on a disk. It is difficult that the DBMS on a disk processes the 500,000 number of insert transaction per a second. So, the DBMS on main memory appeared to use memory. But it is difficultthat very large data stores into the DBMS on a memory because of limited amount of memory. In this paper we propose storage management system using insert transaction of a block unit that can process insert transaction over 50,000 and store data on low storage cost. A transaction of a block unit can decrease cost for a log and index per each tuple as transforming a transaction of a tuple unit to a block unit. Besides, the proposed system come cost to decompress all block of data because the information of each field be loss. To solve the problems, the proposed system generates the index of each compressed block to prevent reducing speed for searching. The proposed system can store very large data generated in semiconductor system and reduce storage cost.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.321-326
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• 2019

Image denoising methods based on block matching are founded on the experimental observations that neighboring patches or blocks in images retain similar features with each other, and have been proved to show superior performance in denoising different kinds of noise. The methods, however, take into account only neighboring blocks in searching for similar blocks, and ignore the characteristic features of the reference block itself. Consequently, denoising performance is negatively affected when outliers of the Gaussian distribution are included in the reference block which is to be denoised. In this paper, we propose an expanded block matching method in which noisy images are first decomposed into a number of bit-planes, then the range of true signals are estimated based on the distribution of pixels on the bit-planes, and finally outliers are replaced by the neighboring pixels belonging to the estimated range. In this way, the advantages of the conventional Gaussian filter can be added to the blocking matching method. We tested the proposed method through extensive experiments with well known test-bed images, and observed that performance gain can be achieved by the proposed method.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.6 s.312
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• pp.57-64
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• 2006

Motion estimation plays an important role for the compression of video signals. The proposed method utilizes an adaptive threshold and characteristics of a distribution of SAD (sum of absolute difference). Generally, the more complex the SAD distribution is, the larger SAD value tends to be. This proposed algorithm tries to reduce the search points in a simple distribution but increase them in a complex distribution to avoid local minima. A macro block is divided into 9 areas. One of them chosen using spatio-temporal correlation is called the primary area and the others are called the secondary area that will be searched to avoid local minima. The proposed algorithm decides if just one area (the primary area or the secondary area) will be enough to be searched or both areas should be searched, using adaptive threshold. Compared with famous motion estimation algorithms, the simulation result shows that the searching points per macro block and MSE decreases about 16.4% and 32.83 respectively on the average.

• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.345-354
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• 2005

Since motion estimation and motion compensation methods remove the redundant data to employ the temporal redundancy in images, it plays an important role in digital video compression. Because of its high computational complexity, however, it is difficult to apply to high-resolution applications in real time environments. If we have a priori knowledge about the motion of an image block before the motion estimation, the location of a better starting point for the search of an exact motion vector can be determined to expedite the searching process. In this paper presents the motion detection algorithm that can run robustly about recusive motion. The motion detection compares and analyzes two frames each other, motion of whether happened judge. Through experiments, we show significant improvements in the reduction of the computational time in terms of the number of search steps without much quality degradation in the predicted image.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.4
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• pp.335-343
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• 2001

The conventional wavelet-based fractal image coding has the disadvantage that the encoding takes a long time, since each range block finds the best domain in the image. In this Paper, we propose wavelet-based fractal image coding using SAS(Self Affine System) method and multi-scale factor. It consists of the range and domain blocks in DWT(discrete wavelet transform) region. Using SAS method, the proposed method is that the searching process of the domain block is not required, and the range block selects the domain which is relatively located the same position in the upper level. The proposed method can perform a fast encoding by reducing the computational complexity in the encoding process. In order to improve the disadvantage of SAS method which is reduced image qualify, the proposed method is improved image qualify using the different scale factors for each level. As a result, there is not influence on an image quality, the proposed method is enhanced the encoding time and compression ratio, and it is able to the progressive transmission.

• The KIPS Transactions:PartB
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• v.9B no.2
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• pp.199-204
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• 2002

Since motion estimation and motion compensation methods remove the redundant data to employ the temporal redundancy in images, it plays an important role in digital video compression. Because of its high computational complexity, however, it is difficult to apply to high-resolution applications in real time environments. If we have information about the motion of an image block before the motion estimation, the location of a better starting point for the search of an exact motion vector can be determined to expedite the searching process. In this paper, we present an adaptive motion estimation approach bated on temporal correlations of consecutive image frames that defines the search pattern and determines the location of the initial search point adaptively. Through experiments, compared with DS(Diamond Search) algorithm, the proposed algorithm is about 0.1∼0.5(dB) better than DS in terms of PSNR(Peak Signal to Noise Ratio) and improves as high as 50% compared with DS in terms of average number of search point per motion vector estimation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.1C
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• pp.79-85
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• 2008

Dirty Paper Coding(DPC) can achieve the sum capacity of a multiuser multiple-input multiple-output(MU MIMO) broadcast channels. However, due to the high computational complexity of the successive encoding and decoding, deploying DPC in real systems is impractical. As one of practical alternatives to DPC, Block Diagonalization(BD) was researched. BD is an extension of the zero-forcing preceding technique that eliminates interuser interference(IUI) in downlink MIMO systems. Though BD has lower complexity than DPC, BD shows poor sum capacity performance. We show that sum capacity performance of BD is degraded due to no IUI constraint. Then, we modify BD to improve its sum capacity performance with relaxing the constraint and sub optimal channel set searching. With simulation results, it can be verified that our modification in BD induces some improvement in sum capacity performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.1C
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• pp.9-15
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• 2007

We propose the fast full search algorithm that reduces the computation of the block matching algorithm which is used for motion estimation of the video coding. Since the conventional spiral search method starts searching at the center of the search window and then moves search point to estimate the motion vector pixel by pixel, it is good for the slow motion pictures. However the proposed method is good for the fast and slow motion because it estimates the motion in the new search order after partitioning the search window. Also, when finding the motion vector, this paper presents the method that reduces the complexity by computing the matching error in the order which is determined by local image complexity. The proposed algorithm reduces the computation up to 99% for block matching error compared with the conventional spiral full search algorithm without any loss of image quality.