• The Journal of Natural Sciences
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• 제5권2호
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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).

• Proceedings of the KIEE Conference
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.980-982
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• 1995

Image coding based on a fractal theory of iterated transformations presents highly compressed image. In this paper, we compress image using the partitioning method which devides image adaptively in horizon and vertical axis. This method can encode image more compactly than the quadtree partitioning method. The maximum range size can be selected as $32{\times}32$ blocks and the minimum size can be $4{\times}4$ blocks. And the domain size is twice as many as the range size.

• The Journal of Natural Sciences
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• 제7권
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• pp.67-73
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• 1995

In this paper, a new texture segmentation-based image coding technique which performs segmentation based on 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 into texturally homogeneous regions with respect to the degree of roughness as perceived by the HVS. 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. It is very important to determine the proper block size for estimating the fractal dimension. Good quality reconstructed images are obtained with about 0.1 to 0.25 bit per pixel (bpp) for many different types of imagery.

• Journal of Korea Multimedia Society
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• 제8권6호
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• pp.776-782
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• 2005

This paper presents a SPIHT image compression method using biorthogonal multi wavelets on [-1,1]. A family of biorthogonal scaling vectors is constructed using fractal interpolation function, and the associated biorthogonal multi wavelets are constructed. This paper uses biorthogonal multi wavelets to be supported in [-1,1] associated with biorthogonal scaling vectors to be supported in [-1,1]. The scaling vectors and wavelets remain biorthogonal when restricted to integer intervals, making them well suited for bounded domains. The experiment results of simulation of the proposed image compression using biorthogonal multiwavelets on [-1,1] based on SPIHT were found to be excellent PSNR for LENA and PEPPERS images except for BABOON image than already existing single wavelets and DGHM multi wavelets.

• Journal of the Institute of Electronics Engineers of Korea SC
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• 제39권3호
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• pp.220-227
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• 2002

An one-dimensional VLSI array for high speed processing of Fractal image compression was designed. Using again the overlapped input data of adjacent domain blocks in the existing one-dimensional VLSI array, we can save the number of total input for the operations, and so we can save the total computation time. In the design procedure, we considered the data dependences between the input data, reordered the input data to the array, and designed the processing elements. Registers and multiplexors are added for the storing and routing of the input data in some processing elements. Consequently as adding a little hardware, this design shows (N-4B)/4(N-B) times of speed-up compared with the existing array, where N is image size and B is block size.

• Journal of the Korean Institute of Telematics and Electronics B
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• 제33B권6호
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• pp.82-91
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• 1996

In this paper, an image coding technique using fractal interpolation is proposed. Similar to the conventional methods, an image is partitioned into blocks and each block is coded independently. However, an interpolation point is ahsared by its neighboring blocks. This means that each block can use all its interpolation points with minimal increase of new data. For a simple implementation, triangular blocks are used instead of square blocks and new coefficients are difined. Data obtained in the encoding process hav estatistical characteristics suitable sfor entropy coding, an dthus arithmetic coding is perfomred for improving the compression efficiency. The results of the proposed coder in comparison with those of a conventional coder show that the interpolation method reduces block effect caused by a memoryless block coder, especially at low bit rates. This improvement is due to sharing of information between adjacent blocks. Moreover, th enumber of iteration required in ecoding process is reduced since more information is used to decode each block.

• Journal of Korea Multimedia Society
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• 제7권5호
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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.

• Journal of the Korean Institute of Telematics and Electronics B
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• 제33B권11호
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• pp.62-71
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• 1996

In this paper we propose a method that improves PSNR at low bit rate and reduces computational complexity in fractal image coding based on discrete wavelet transform. The proposed method, which uses significant coefficient tree, improves PSNR of the reconstructed image and reduces computational comlexity of mapping domain block onto range block by matching only the significant coefficients of range block to coefficients of domain block. Also, the proposed method reduces error propagation form lower resolution subbands to higher resolution subbands by correcting error of lower resolution subbands. Some experimental results confirm that the proposed method reduces encoding and decoding time significantly and has fine reconstructed images having no blocking effect and clear edges at low bit rate.

• Journal of Biomedical Engineering Research
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• 제21권3호
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• pp.247-254
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• 2000

We propose an efficient MR image compression technique using fractal coding in wavelet transform domain. In the Proposed method , we construct significant coefficient trees with the absolute values of discrete wavelet transform coefficients and then perform the fractal coding with the information of significant coefficients having high energy. For MR images, most Pixels including background have very low gray level values, the number of significant coefficients is small. so we can expect high compression rate. In addition. since this method uses the fractal coding in wavelet transform domain, blocking artifact is reduced prominently and edges sensitive to human visual system are well preserved. As a result of computer simulation, we obtained the reconstructed images with better quality than those by JPEG at the low bit rates below 0.33[bpp].

• Journal of the Korean Institute of Telematics and Electronics T
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• 제35T권3호
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• pp.15-24
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• 1998

In this paper, we present new method of fractal image coding based on iterated function system(IFS) suggested by Barnsley. In previous Fractal coding using full searching algorithm, the quality of reconstructed image was better than other fractal coding method's, but it took a long time in that algorithm for searching domain blocks matched. And it is performed through linear affine transform, therefore it is difficult to approximate the complex range blocks. In this paper, using quadtree partitioning, complex blocks are divided into more smaller blocks, and simple blocks are merged to more larger blocks. So, we can got more precisely approximated range blocks and reduce the number of transformations. Hence, we have improved the compression ratio. In addition, we restrict the region of searching domains in order to reduce the searching time and coding time. Compared with full searching algorithm, we reduced coding time drastically, and quality of reconstructed image was better in terms subjective criteria. And compared with Monro's, our method is slower, but we could obtain a reconstructed image with better quality.