• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.8
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• pp.99-108
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• 1994

In this paper an image compression technique based on fractal theory using iterated contractive transformation is analysed and an improved image coder is suggested. Existing methods used the classifier proposed by Ramamurthi and Gersho which utilize the properties of neighboring pixels in the spatial domain. In this paper DCT-based classification is applied to 512$\times$512 images and PSNR improvement of 0.4~2.7 dB is obtained at lower bit rate over conventional algorithms. In addition the effect of varying the domain block size and quantization step size of the luminance shift parameter on the compression ratio and the image quality is compared and analysed.

• Journal of the Korea Society of Computer and Information
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• v.3 no.4
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• pp.97-102
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• 1998

In this paper, 1 proposed methods of fractal image compression to get better orginal images in a high compression raitos with encoding image effectively. For given signal-to-noise raitos tolerances, show the proposed compression method are higher than Fisher's method for compression raitos at all thresholds. This is due to improve compression time in the local IFS structure that reduce PIFS information storage requirements from 48bits range blocks to the 40bits.

• Journal of the Institute of Electronics Engineers of Korea TE
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• v.39 no.3
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• pp.35-41
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• 2002

There are many image segmentation methods having been published as the results of research so far, but it is difficult to be partitioned to each similar range that should be extracted into the accurate parameters of image information on the images with noises. Also if it is used to fractal coding, according to amount of noise in image, the image segmentation leads to decreasing of the compression ratio. In this paper, we propose the new quad-tree image segmentation using the box-counting dimension which can estimate the effective image information parameters against the noise properties and apply this method to fractal image coding. As the result of simulation, we confirm that the image segmentation is improved to 31.10% for parameter detection of image information and compression ratio is enhanced to 38.93% for fractal image coding when tested on 10% Gaussian white noise image by the proposed quad-tree method compared with method using existing quad-tree. 

• The KIPS Transactions:PartB
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• v.9B no.6
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• pp.715-726
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• 2002

In this paper, we construct real-time moving picture encoder based on fractal theory by using general purpose digital signal processors. The constructed encoder is implemented using two fixed-point general DSPs (ADSP2181) and performs image encoding by three stage pipeline structure. In the first pipeline stage, the image grabber acquires image data from NTSC standard image signals and stores digital image into frame memory. In the second stage, the main controller encode image dada using fractal algorithm. The last stage, output controller perform Huffman coding and result the coded data via RS422 port. The performance tests of the constructed encoder shows over 10 frames/sec encoding speed for QCIF data when all the frames are encoded. When we encode the images using the interframe and redundency based on the proposed algorithms, encoding speed increased over 30 frames/sec in average.

• The Journal of Natural Sciences
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• v.4
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• pp.191-220
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• 1991

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. This method solves the problems of a segmentation-based image coding technique with constant segments by proposing a methodology for segmenting an image texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. The fractal dimension is used to measure the roughness of the textural regions. 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. For the boundaries, a binary image representing all the boundaries is created. For regions belonging to perceived constant intensity, only the mean intensity values need to be transmitted. The smooth and rough texture regions are modeled first using polynomial functions, so only the coefficients characterizing the polynomial functions need to be transmitted. The bounda-ries, the means and the polynomial functions are then each encoded using an errorless coding scheme. Good quality reconstructed images are obtained with about 0.08 to 0.3 bit per pixel for three different types of imagery ; a head and shoulder image with little texture variation, a complex image with many edges, and a natural outdoor image with highly textured areas.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.36C no.12
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• pp.11-19
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• 1999

Fractal image compression algorithm has been studied mostly not in the view of hardware but software. However, a general processor by software can't decode fractal compressed images in real-time. Therefore, it is necessary that we develop a fast dedicated hardware. However, design examples of dedicated hardware are very rare. In this paper, we designed a quadtree fractal-based compressed image decoder which can decode $256{\times}256$ gray-scale images in real-time and used two power-down methods. The first is a hardware-optimized simple post-processing, whose role is to remove block effect appeared after reconstruction, and which is easier to be implemented in hardware than non-2' exponents weighted average method used in conventional software implementation, lessens costs, and accelerates post-processing speed by about 69%. Therefore, we can expect that the method dissipates low power and low energy. The second is to design a power dissipation in the multiplier can be reduced by about 28% with respect to a general array multiplier which is known efficient for low power design in the size of 8 bits or smaller. Using the above two power-down methods, we designed decoder's core block in 3.3V, 1 poly 3 metal, $0.6{\mu}m$ CMOS technology.

• Journal of the Korea Computer Industry Society
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• v.2 no.3
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• pp.297-308
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• 2001

We propose an algorithm which can reconstruct the 3D information from geographical information. The conventional techniques, the triangular patches and the Random Fractal Midpoint Displacement (RFMD) method, etc., have often been used to reconstruct natural images. While the RFMD method using Gaussian distribution obtains good results for the symmetric images, it is not reliable on asymmetric images immanent in the nature. Our proposed algorithm employs neural networks for the RFMD method to present the asymmetrical images. By using a neural network for reconstructing the 3D images, we can utilize statistical characteristics of irregular data. We show that our algorithm has a better performance than others by the point of view on the similarity evaluation. And, it seems that our method is more efficient for the mountainous topography which is more rough and irregular.

• Imaging Science in Dentistry
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• v.33 no.2
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• pp.113-119
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• 2003

Purpose : To evaluate the relationship between morphometric analysis of bone microstructure from digital radiographic image and trabecular bone strength. Materials and Methods : One hundred eleven bone specimens with 5 mm thickness were obtained from the mandibles of 5 pigs. Digital images of specimens were taken using a direct digital intraoral radiographic system. After selection of ROI (100 × 100 pixel) within the trabecular bone, mean gray level and standard deviation were obtained. Fractal dimension and the variants of morphometric analysis (trabecular area, periphery, length of skeletonized trabeculae, number of terminal point, number of branch point) were obtained from ROI. Punch sheer strength analysis was performed using Instron (model 4465, Instron Corp., USA). The loading force (loading speed 1 mm/min) was applied to ROI of bone specimen by a 2 mm diameter punch. Stress-deformation curve was obtained from the punch sheer strength analysis and maximum stress, yield stress, Young's modulus were measured. Results: Maximum stress had a negative linear correlation with mean gray level and fractal dimension significantly (p<0.05). Yield stress had a negative linear correlation with mean gray level, periphery, fractal dimension and the length of skeletonized trabeculae significantly (p < 0.05). Young's modulus had a negative linear correlation with mean gray level and fractal dimension significantly (p < 0.05). Conclusions : The strength of cancellous bone exhibited a significantly linear relationship between mean gray level, fractal dimension and morphometric analysis. The methods described above can be easily used to evaluate bone quality clinically.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3185-3199
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• 1997

This paper suggests techniques to reduce coding time which is a problem in traditional fractal compression and to improve fidelity of reconstructed images by determining fractal coefficient through adaptive selection of block approximation formula. First, to reduce coding time, we construct a linear list of domain blocks of which characteristics is given by their luminance and variance and then we control block searching time according to the first permissible threshold value. Next, when employing three-level block partition, if a range block of minimum partition level cannot find a domain block which has a satisfying approximation error, we choose new approximation coefficients using a non-linear approximation of luminance term. This boosts the fidelity. Our experiment employing the above methods shows enhancement in the coding time more than two times over traditional coding methods and shows improvement in PSNR value by about 1-3dB at the same com- pression rate.

• 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.