• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1362-1364
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• 1996

In this paper a Dear lossless compression of medical images with vector quantizer is proposed. In order to apply the vector quantizer to medical images, the peak error in the reconstructed image is reduced down to 1. Simulation results show that the proposed coding scheme provides better performance with a PSNR improvement compared to the conventional JPEG standard.

• The KIPS Transactions:PartB
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• v.13B no.3 s.106
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• pp.231-238
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• 2006

In this paper, the method to make lossless image compression that holds considerable part of total volume of mobile game has been proposed. To increase the compression rate, we compress the image by Deflate algorithm defined in RFC 1951 after reorganize it at preprocessing stage before conducting actual compression. At the stage of preprocessing, we obtained the size of a dictionary based on the information of image which is the feature of Dictionary-Based Coding, and increased the better compression rate than compressing in a general manner using in a way of restructuring image by pixel packing method and DPCM prediction technique. It has shown that the method increased 9.7% of compression rate compare with existing mobile image format, after conducting the test of compression rate applying the suggested compression method into various mobile games.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10C
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• pp.961-967
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• 2009

In this paper, the lossless compression method is proposed for an 8-bit bitplane of the input image. The lower bitplanes are not well compressed because of irregularity of pixels. To overcome these drawbacks, this paper propose a mixed coding method that using the block-based lossless compression and the bit-based losselss compression, introducing the H. 264 and the JBIG. First, to take advantage of the characteristics of the bitplanes, 8-bitplane against the top 4 bits and lower 4 bits were separated. Next, the JBIG compression method was used in separated top 4-bitplane because of a lot of correlation between bits. And a separated lower 4-bitplane was applied the improved method that using the H. 264 lossless prediction. A pre-processing method applied to the lower 4-bitplane then irregular distribution of pixel values are converted to regular. Using the proposed method to test for various test images were performed. Experimental results from a printer using 8-bit image compared to JBIG average 19%, lower 4bit image compression performance with an average of 11% could be obtained.

• Journal of Korea Society of Industrial Information Systems
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• v.5 no.1
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• pp.102-107
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• 2000

In general text compression techniques cannot be used directly in image compression because the model of text and image are different Recently, a new class of text compression, namely, block-sorting algorithm which involves Burrows and Wheeler transformation(BWT) gives excellent results in text compression. However, if we apply it directly into image compression, the result is poor. So, we propose simple method in order to improve the lossless compression performance of image. The proposed method can be divided into three steps. It is decomposed into ten subbands with the help of symmetric short kernel filter. The resulting subbands are block-sorted according to the method by BWT, and the redundancy is removed with the help of an adaptive arithmetic coder. Experimental results show that the proposed method is better than lossless JPEG and LZ-based compression method(PKZIP).

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.756-761
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• 1998

This paper describes an in-line lossless data compression method using adaptive binary arithmetic coding. To achieve better compression efficiency , we employ an adaptive fuzzy -tuning modeler, which uses fuzzy inference to deal with the problem of conditional probability estimation. The design is simple, fast and suitable for VLSI implementation because we adopt the table -look-up approach. As compared with the out-comes of other lossless coding schemes, our results are good and satisfactory for various types of source data.

• Journal of Korea Society of Digital Industry and Information Management
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• v.6 no.2
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• pp.11-17
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• 2010

In this paper, we propose efficient rate control algorithm for video near-lossless compression. The proposed method adjusts pixel error range using amounts of previous encoded bits and target bits at a slice encoding interval. Thus, encoded bits by the proposed method are transmitted well through limited bandwidth without overflow. Also, target bits and encoded bits can be controlled according to transmission policy. Experimental results show that our proposed method not only almost fits compression ratio, but also has better image quality.

• Proceedings of the KOSOMBE Conference
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• v.1996 no.11
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• pp.293-296
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• 1996

In this paper, we studied two image characteristics, similarity and smoothness, which give rise to local and global redundancy in image representation. The similarty means that any patterns in the image repeat itself anywhere in the rest of image. The smoothness means that the gray level values within a given block vary gradually rather than abruptly. In this sense, we propose a lossless medical image compression scheme which exploits both types of redundancy. This method segments the image into variable size blocks and encodes them depending on characteristics of the block. The proposed compression schemes works better than other compression schemes such as the huffman, the arithmetic, the Lempel-Ziv and the lossless scheme of JPEG.

• Journal of KIISE:Software and Applications
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• v.37 no.3
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• pp.170-176
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• 2010

Prediction is an important step in high-performance lossless data compression. In this paper, we propose a novel lossless image coding algorithm to increase prediction accuracy which can display low-resolution images quickly with a multi-resolution image technique. At each resolution, we use pixels of the previous resolution image to estimate current pixel values. For each pixel, we determine its estimated value by considering horizontal, vertical, diagonal edge information and average, weighted-average information obtained from its neighborhood pixels. In the experiment, we show that our method obtains better prediction than JPEG-LS or HINT.

• Journal of Broadcast Engineering
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• v.16 no.6
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• pp.962-968
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• 2011

The conventional lossless compression of color images is to apply a compression method to each of color components separately, without considering the channel correlation. There had been several methods that consider the channel correlation, but they were confined to the compression of satellite or aerial images only, and the performance of these algorithms to general photos is not satisfactory. This paper proposes a new lossless color image compression method that exploits the correlation between the color components. Specifically, asymmetric sampling is applied to transform an image into mosaic image and the rest, which are compressed separately. By using the information from the compressed mosaic image, the rest images are predicted for further reducing the information to be compressed. Experimental results show that the proposed method improves the compression performance by 35% over the conventional separate compression methods and 10% over the existing methods that exploit the channel correlation.

• Journal of the Institute of Convergence Signal Processing
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• v.3 no.1
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• pp.33-40
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• 2002

In this paper, we proposed a lossless compression algorithm of medical images which is essential technique in picture archive and communication system. Mammographic image and magnetic resonance image in among medical images used in this study, proposed a region growing segmentation algorithm for compression of these images. A proposed algorithm was partition by three sub region which error image, discontinuity index map, high order bit data from original image. And generated discontinuity index image data and error image which apply to a region growing algorithm are compressed using JBIG(Joint Bi-level Image experts Group) algorithm that is international hi-level image compression standard and proper image compression technique of gray code digital Images. The proposed lossless compression method resulted in, on the average, lossless compression to about 73.14% with a database of high-resolution digital mammography images. In comparison with direct coding by JBIG, JPEG, and Lempel-Ziv coding methods, the proposed method performed better by 3.7%, 7.9% and 23.6% on the database used.