• Proceedings of the Korea Society of Information Technology Applications Conference
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• 2005.11a
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• pp.169-172
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• 2005

Genetic algorithm based predictor for lossless image compression is propsed. We describe a genetic algorithm to learn predictive model for lossless image compression. The error image can be further compressed using entropy coding such as Huffman coding or arithmetic coding. We show that the proposed algorithm can be feasible to lossless image compression algorithm.

• Journal of Broadcast Engineering
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• v.9 no.3
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• pp.246-256
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• 2004

In this paper, we proposed a new lossless image compression algorithm. Lossless image compression has been used in the field that requires the accuracy and precision. Thus, application areas using medical unaging, prepress unaging, image archival systems, precious artworks to be preserved, and remotely sensed images require lossless compression. The compression ratio from lossless image compression has not been satisfactory, thus far. So, new method of lossless image compression has been investigated to get better compression efficiency. We have compared the compression results with the most typical compression methods such as CALIC and JPEG-LS. CALIC has shown the best compression-ratio among the existing lossless coding methods at the cost of the extensive complexity by three pass algorithm. On the other hand, JPEG-LS's compression-ratio is not higher than CALIC, but was adopted as an international standard of ISO because of the low complexity and fast coding process. In the proposed method, we adopted an adaptive predictor that can exploit the characteristics of individual images, and an adaptive arithmetic coding with multiple probability models. As a result, the proposed algorithm showed 5％ improvement in compression efficiency in comparison with JPEG-LS and showed comparable compression ratio with CALIC.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.3
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• pp.124-130
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• 2006

A wavelet-based image compression system allowing both lossless and lossy image compression is proposed in this paper. The proposed algorithm consists of the two stages. The first stage uses the wavelet packet transform and the quad-tree coding scheme for the lossy compression. In the second stage, the residue image taken between the original image and the lossy reconstruction image is coded for the lossless image compression by using the integer wavelet transform and the context based predictive technique with feedback error. The proposed wavelet-based algorithm, allowing an optional lossless reconstruction of a given image, transmits progressively image materials and chooses an appropriate wavelet filter in each stage. The lossy compression result of the proposed algorithm improves up to the maximum 1 dB PSNR performance of the high frequency image, compared to that of JPEG-2000 algorithm and that of S+P algorithm. In addition, the lossless compression result of the proposed algorithm improves up to the maximum 0.39 compression rates of the high frequency image, compared to that of the existing algorithm.

• International Journal of Computer Science & Network Security
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• v.23 no.11
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• pp.73-76
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• 2023

We introduce DCNN and DRAE appraoches for compression of medical videos, in order to decrease file size and storage requirements, there is an increasing need for medical video compression nowadays. Using a lossy compression technique, a higher compression ratio can be attained, but information will be lost and possible diagnostic mistakes may follow. The requirement to store medical video in lossless format results from this. The aim of utilizing a lossless compression tool is to maximize compression because the traditional lossless compression technique yields a poor compression ratio. The temporal and spatial redundancy seen in video sequences can be successfully utilized by the proposed DCNN and DRAE encoding. This paper describes the lossless encoding mode and shows how a compression ratio greater than 2 (2:1) can be achieved.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.285-290
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• 2009

In this paper, we propose a new algorithm that improves the lossless data compression rate. The proposed algorithm lessens the redundancy and improves the compression rate evolutionarily around 40 up to 80 percentile depending on the characteristics of binary images used for compression. In order to demonstrate the superiority of the proposed method, the comparison between the proposed method and the LZ78 (LZ77) is demonstrated through experimental results theoretical analysis.

• Journal of Korea Society of Digital Industry and Information Management
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• v.5 no.3
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• pp.141-145
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• 2009

In this paper, we propose an efficient lossless compression algorithm using spatial and temporal information. The proposed method obtains higher lossless compression of images than other lossless compression techniques. It is divided into two parts, a motion adaptation based predictor part and a residual error coding part. The proposed nonlinear predictor can reduce prediction error by learning from its past prediction errors. The predictor decides the proper selection of the spatial and temporal prediction values according to each past prediction error. The reduced error is coded by existing context coding method. Experimental results show that the proposed algorithm has better performance than those of existing context modeling methods.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.7
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• pp.1003-1008
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• 2013

This paper proposes a compression scheme based on the modified reversible integer transform (MRIT) and forward adaptive prediction for lossless image compression. JPEG XR is the newest image coding standard with high compression ratio and that composed of the Photo Core Transform (PCT) and backward adaptive prediction. To improve the efficiency and quality of compression, we substitutes the PCT and backward adaptive prediction for the modified reversible integer transform (MRIT) and forward adaptive prediction, respectively. Experimental results indicate that the proposed method are superior to the previous method of JPEG XR in terms of lossless compression efficiency and computational complexity.

• Journal of Information Processing Systems
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• v.12 no.2
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• pp.322-331
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• 2016

Image compression is an essential technique for saving time and storage space for the gigantic amount of data generated by images. This paper introduces an adaptive source-mapping scheme that greatly improves bit-level lossless grayscale image compression. In the proposed mapping scheme, the frequency of occurrence of each symbol in the original image is computed. According to their corresponding frequencies, these symbols are sorted in descending order. Based on this order, each symbol is replaced by an 8-bit weighted fixed-length code. This replacement will generate an equivalent binary source with an increased length of successive identical symbols (0s or 1s). Different experiments using Lempel-Ziv lossless image compression algorithms have been conducted on the generated binary source. Results show that the newly proposed mapping scheme achieves some dramatic improvements in regards to compression ratios.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.05a
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• pp.268-272
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• 2003

Large medical images in PACS are compressed for saving storage space and improving network speed. The integrated compression environment was designed and developed for uniting of various compression methods. Various compression algorithm-RLE compression, lossless JEPG, JPEG, was built into it, complying with DICOM. A image compression using DWT was also implemented in it. And a unified algorithm of lossless compression and lossy compression was designed to improve images quality and to make compression ratios high. And integrated compression environment was operating together with a database program for efficient and user-friendly management.

• ETRI Journal
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• v.28 no.2
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• pp.260-263
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• 2006

This letter proposes a new near-lossless image compression method with only one line buffer cost for a digital camera with Bayer format image. For such format data, it can provide a low average compression rate (4.24bits/pixel) with high-image quality (larger than 46.37dB where the error of every pixel is less than two). The experimental results show that the near-lossless compression method has better performance than JPEG-LS (lossless) with ${\delta}$ = 2 for a Bayer format image.