• The Transactions of the Korea Information Processing Society
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• v.3 no.5
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• pp.1317-1328
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• 1996

In this paper, we propose a fast image coding algorithm to shorten long time to take on fractal image encoding. For its Performance evaluation, the algorithm compares with other traditional fractal coding methods. In the traditional fractal image coding methods, an original image is contracted by a factor in order to make the corresponding image to be compared with. Them, the whole area of the contracted image is searched in order to find the fixed point of contractive transformation of the orignal image corresponding to the contracted image. It needs a lot of searching time on encoding However, the proposed algorithm considerable reduces encoding time by using scaling method and limited search area method. On comparison of the proposed algorithm with Joaquin's method, the proposed algorithm is at least 180 times as fast as that of Jacquin's method on encoding time with a little degradation of the decoded image quality and a little increase of the compression rate. There-for, it is found that the proposed algorithm largely improves the performance in the aspect of encoding time when compared with other fractal image coding methods.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.179-187
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• 2000

Fractal theory has strengths such as high compression rate and fast decoding time in application to image compression, but it suffers from long comparison time necessary for finding an optimally similar domain block in the encoding stage. This paper proposes a neural network based block classifier which enhances the encoding time significantly by classifying domain blocks into 4 patterns and searching only those blocks having the same pattern with the range block to be encoded. Size of a block is differently determined depending on the image complexity of the block. The proposed algorithm has been tested with three different images having various featrues. The experimental results have shown that the proposed algorithm enhances the compression time by 40% on average compared to the conventional fractal encoding algorithms, while maintaining allowable image qualify of PSNR 30 dB.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.2
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• pp.13-19
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• 2001

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 that each range block was classified to iterated and non-iterated regions. Non-iterated regions is called data dependency region, and if data dependency region extended, IFS regions are contractive. In this paper, a searched region of the domain is limited to the range regions that is similar with the domain blocks, and the domain region is more overlapped. As a result, data dependency region has maximum region, that is IFS regions can be minimum region. The minimizing method of domain region is defined to minimum domain(MD) which is minimum IFS region. Using the minimizing method of domain region, there is not influence PSNR(peak signal-to-noise ratio). And it can be performed a fast decoding by reducing the computational complexity for IFS in fractal image decoding.

• The Magazine of the IEIE
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• v.21 no.10
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• pp.13-21
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• 1994

• The Transactions of the Korea Information Processing Society
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• v.5 no.4
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• pp.1048-1056
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• 1998

In this paper, a fractal image coding based on discrete wavelet transform is proposed to improve PSNR at low bit rates and reduce computational complexity of encoding process. The proposed method takes the absolute value of discrete wavelet transform coefficients, and then constructs significant coefficients trees, which indicate the positions and signs of the significant coefficients. This method improves PSNR and reduces computational complexity of mapping contracted domain pool onto range block, by matching only the significant coefficients of range block to coefficients of contracted domain block. Also, this paper proposes a classification scheme which minimizes the number of contracted domain blocks compared with range block. This scheme significantly reduces the number of range and contracted domain block comparison.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12A
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• pp.2059-2066
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• 2001

In this paper, we proposed a method to reduce the number of input pins in one-dimensional VLSI array for fractal image compression. We use quad-tree partition scheme and can reduce the number of the input pins up to 50% by sharing the domain\`s and the range\`s data input pins in the proposed VLSI array architecture. Also, we can reduce the input pins and simplify the internal operation circuit of the processing elements by eliminating a few number of bits of the least significant bits of the input data. We simulated using the 256$\times$256 and 512$\times$512 Lena images to verify performance of the proposed method. As the result of simulation, we can decompress the original image with about 32dB(PSNR) in spite of elimination of the least significant 2-bit in the original input data, and additionally reduce the number of input pins up to 25% compared to VLSI array sharing input pins of range and domain.

• Journal of the Korean Society of Radiology
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• v.13 no.4
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• pp.509-516
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• 2019

In the linear transformation method, the original image is divided into a plurality of range blocks, and a partial transform system for finding an optimal domain block existing in the image for each range block is used to adjust the performance of the compression ratio and the picture quality, The nonlinear transformation method uses only the rotation transformation among eight shuffle transforms. Since the search is performed only in the limited domain block, the coding time is faster than the linear transformation method of searching the domain block for any block in the image, Since the optimal domain block for the range block can not be selected in the image, the performance may be lower than other methods. Therefore, the nonlinear transformation method improves the performance by increasing the approximation degree of the brightness coefficient conversion instead of selecting the optimal domain block, The smaller the size of the block, the higher the PSNR value, The higher the compression ratio is increased groups were quadtree block divided to encode the image at best.

• The Transactions of the Korea Information Processing Society
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• v.5 no.10
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• pp.2704-2712
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• 1998

In this paper, a MRI image compression technique, which allocates bits effectively by using lossless coding for region having important infommtion to decide disease and lossy coding for the rest, is proposed. In the proposed method, for MHI images needed to rccognize disk disease, we recognizc region having important objects by using the characteristics of c1isease. As the recognized region is imrxlrtant to decide whether disease exists or not, it is compressed by lossless coding and the rest is compressed by lossy coding, Also for the region compressed by lossy coding, we can obtain fine reconstructed images without blocking effect by adopting fractal coding in wavelet transform domain.

• The Journal of Natural Sciences
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• v.5 no.2
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• pp.33-38
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• 1992

A new texture segmentation-based image coding technique which performs segmentation based on roughness of textural regions and properties of the human visual system (HVS) is presented for multime-dia teleconference. The segmentation is accomplished by thresholding the fractal dimension so that textural regions are classified into three texture classes; perceived constant intensity, smooth texture, and rough texture. An image coding system with high compression and good image quality is achieved by developing an efficient coding technique for each segment boundary and each texture class. We compare the coding efficiency of this technique with that of a well established technique (discrete cosine transform (DCT) image coding).

• Journal of Korea Multimedia Society
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• v.7 no.5
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• pp.658-667
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• 2004

For digital image compression, various fractal image coding schemes using the self-similarity of image have been studied extensively. This paper discusses the problem that occurs during the calculating process of adaptive orthogonal transformation and provides improvements of LIFS coding scheme using the transformation. This proposed scheme has a better performance than JPEG for a wide range of compression ratio. This research also proposes an image composition method consisting of all domains of the transformation. The results show that the arithmetic operation processes of the encoder and the decoder become much smaller even without the distortion of the coding performance.