• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.7B
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• pp.1378-1383
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• 1999

In this paper, an effective variable-block-based image compression technique using wavelet transform is proposed. Since the statistical property of each wavelet subband is different, we apply the adaptive quantization to each wavelet subband. In the proposed algorithm, each subband is divided into non-overlapping variable-sized blocks based on directional properties. In addition, we remove wavelet coefficients which are below a certain threshold value for coding efficiency. To compress the transformed data, the proposed algorithm quantizes the wavelet coefficients using scalar quantizer in LL subband and vector quantizers for other subbands to increase compression ratio. The proposed algorithm shows improvements in compression ratio as well as PSNR compared with the existing block-based compression algorithms. In addition, it does not cause any blocking artifacts in very low bit rates even though it is also a block-based method. The proposed algorithm also has advantage in computational complexity over the existing wavelet-based compression algorithms since it is a block-based algorithm.

• Journal of the Korean Mathematical Society
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• v.33 no.4
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• pp.955-982
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• 1996

In surface compression applications, one of the main issues is how to efficiently store and calculate the computer representation of certain surfaces. This leads us to consider a nonlinear approximation by box splines with free knots since, for instance, the nonlinear method based on wavelet decomposition gives efficient compression and recovery algorithms for such surfaces (cf. [12]).

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.531-532
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• 2007

According to the characteristics of power signal, the compression algorithm based on wavelet packet transform and denoising is presented. Its performance is assessed in terms of percentage of zero coefficients and energy retanied in reconstructed signals.

• Journal of Biomedical Engineering Research
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• v.18 no.4
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• pp.339-348
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• 1997

In this paper, we propose a coding method to improve compression efficiency for MR image. This can be achieved by combining coding scheme and segmentation scheme which removes noisy background region, which is meaningless for diagnosis in the MR image. In segmentation algoritm, we use full-resolution wavelet transform to extract features of regions in image and Kohonen self-organizing map to classify the features. The subsequent wavelet coder encodes only diagnostically significant foreground regions refering to segmentation map. Our proposed algorithm provides about 15% of bit rate reduction when compared with the same coder which is not combined with segmentation scheme. And the proposed scheme shows better reconstructed image quality than JPEG at the same compression ratio.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.5
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• pp.33-40
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• 2004

In this paper, we constructs face database by using wavelet comparison, and compare face recognition rate by using principle component analysis (Principal Component Analysis : PCA) algorithm. General face recognition method constructs database, and do face recognition by using normalized size. Proposed method changes image of normalized size (92${\times}$112) to 1 step, 2 step, 3 steps to wavelet compression and construct database. Input image did compression by wavelet and a face recognition experiment by PCA algorithm. As well as method that is proposed through an experiment reduces existing face image's information, the processing speed improved. Also, original image of proposed method showed recognition rate about 99.05%, 1 step 99.05%, 2 step 98.93%, 3 steps 98.54%, and showed that is possible to do face recognition constructing face database of large quantity.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.2315-2318
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• 2003

The Wavelet Transform has been applied in mathematics and computer sciences. Numerous studies have proven its advantages in image processing and data compression, and have made it a basic encoding technique in data compression standards like JPEG2000 and MPEG-4. Software implementations of the Discrete Wavelet Transform (DWT) appears to be the performance bottleneck in real-time systems in terms of performance. And hardware implementations are not flexible. Therefore, FPGA implementations of the DWT has been a topic of recent research. The goal of this thesis is to investigate of FPGA implementations of the DWT Processor for image compression applications. The DWT processor design is based on the Lifting Based Wavelet Transform Scheme, which is a fast implementation of the DWT The design uses various techniques. The DWT Processor was simulated and implemented in a FLEX FPGA platform of Altera

• Journal of the Korea Institute of Information and Communication Engineering
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• v.6 no.6
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• pp.980-985
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• 2002

In this paper, we analyzed satellite image with a high resolution compression performance. We need much time in a fast processing on vast satellite image pixel data. On compressing and decompressing, we should keep the information about road, building, forest, etc. In conclusion, we did analyze and compare the performance of compression and decompression for JPEG and WSQ(wavelet scalar quantization) method. As a result, we knew that WSQ was more efficient than JPEG.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.7
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• pp.1624-1632
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• 1996

Wavelet transform is widely used for high compression ratio image compression. It requeires a large memory when it is implemented by a hardware. Therefore, it is efficient to divide the entire image into blocks. Because the wavelet transform for divided blocks causes losses, pixels of the adjacent blocks are used. In the case of color image compression, the image is decomposed into brightness and color components, and then color components are downsampled. When the wavelet transform is performed by using pixels of adjacentblocks, the number of necessary pixels are doubled due to downsampling of color components. In this paper, we propose an efficient block wavelet transform using variablefilter length for brightness and color components. By using the proposed method, the number of pixels of adjacent blocks is optimized. We show the degradation of image quality due to the reduction of filter length for color components is negligible through simulations.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.3
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• pp.493-505
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• 1997

In this paper, a wavelet image compression method using human visually estimated quantizer is proposed. The quantizer has three components. These are constructed by using effects of frequency band, background luminance, and spatial masking. The first quantization factor is a fixed constant value for each band. The second factor is calculated by averaging four wavelet coefficients in the lowest frequency band. The third factor is determined by the difference between wavelet coefficients in the lowest frequency band. Arithmetic coding is used for encoding quantized wavelet coefficients. Coefficients in the lowest band are transmitted without loss. Therefore the compressed image is decompressed by using three quantization factors which can be calculated in the receiver. Compared with previous image compression methods which adopted human visual system, the proposed method shows improved results with less computational cost.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.625-629
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• 2005

According to the rapid development of the information and communication technology, the demand on the efficient compression technology for the multimedia data is increased magnificently. In this Paper, we designed new compression algorithm structure using wavelet base for the compression of ECG signal and audible signal data. We examined the efficiency of the compression between 2-band structure and wavelet packet structure, and investigated the efficiency and reconstruction error by wavelet base function using Daubechies wavelet coefficient and Coiflet coefficient for each structure. Finally, data were compressed further more using Huffman code, and resultant Compression Rate(CR) and Percent Root Mean Square difference(PRD) were compared with those of existent DCT.