• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2761-2781
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• 2018

With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.

• 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 Information Processing Systems
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• v.19 no.2
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• pp.232-239
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• 2023

Aiming at the poor compression quality of traditional image compression coding (ICC) algorithm, a multi-description ICC algorithm based on depth learning is put forward in this study. In this study, first an image compression algorithm was designed based on multi-description coding theory. Image compression samples were collected, and the measurement matrix was calculated. Then, it processed the multi-description ICC sample set by using the convolutional self-coding neural system in depth learning. Compressing the wavelet coefficients after coding and synthesizing the multi-description image band sparse matrix obtained the multi-description ICC sequence. Averaging the multi-description image coding data in accordance with the effective single point's position could finally realize the compression coding of multi-description images. According to experimental results, the designed algorithm consumes less time for image compression, and exhibits better image compression quality and better image reconstruction effect.

• Journal of Internet Technology
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• v.21 no.4
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• pp.1013-1024
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• 2020

In this paper, we introduce an image encryption algorithm that can be used in combination with compression algorithms. Existing encryption algorithms focus on either encryption strength or speed without compression, whereas the proposed algorithm improves compression efficiency while ensuring security. Our encryption algorithm decomposes images into pixel values and pixel intensity subsets, and computes the order of permutations. An encrypted image becomes unpredictable after permutation. Order permutation reduces the discontinuity between signals in an image, increasing compression efficiency. The experimental results show that the security strength of the proposed algorithm is similar to that of existing algorithms. Additionally, we tested the algorithm on the JPEG and the JPEG2000 with variable compression ratios. Compared to existing methods applied without encryption, the proposed algorithm significantly increases PSNR and SSIM values.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.4
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• pp.902-908
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• 2013

This paper suggests to compress data selectively for improvement of data compression efficiency, not to perform unconditional compression on data. Whether to compress or not is determined by selective data distinction. By doing so, we can avoid unnecessary compression in the case of low compression efficiency. Cutting down the unnecessary compression, we can improve the performance of the pre-compression algorithm. Especially, the data algorithm which was already compressed could not be compressed efficiently in many cases, even if apply compression algorithm again. Even in these cases, we don't have to compress data unnecessarily. We implemented the proposed function actually and performed experiments with implementation. The experimental results showed normal operation.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.10
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• pp.1935-1942
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• 2017

Recently, a compression algorithm is widely used as a method for compressing geographic information data. However, such a compression algorithm has a problem that the continuity of the map data is insufficient to actually apply it to the geographic information data compression, and the compression rate is lowered because it can not be compressed into a single data. Therefore, in this paper, we have designed and implemented a Run-Length / Byte-Packing compression algorithm that enables compression of geographic information by combining the advantages of compression algorithms and improves compression and restoration speed. As a result of evaluating the implemented algorithm, it was confirmed that the proposed algorithm improved about 5% on average compared with the existing compression algorithm, and it was confirmed that the compression rate and the restoration speed were improved.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.446-449
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• 2002

This paper represents a improved data compression algorithm of mixed text file by 2 byte completion Hangout and 1 byte alphabet from. Original LZW algorithm efficiently compress a alphabet text file but inefficiently compress a 2 byte completion Hangout text file. To solve this problem, data compression algorithm using 2 byte prefix field and 2 byte suffix field for compression table have developed. But it have a another problem that is compression ratio of alphabet text file decreased. In this paper, we proposes improved LZW algorithm, that is, compression table in the Extended LZW(ELZW) algorithm uses 2 byte prefix field for pointer of a table and 1 byte suffix field for repeat counter. where, a prefix field uses a pointer(index) of compression table and a suffix field uses a counter of overlapping or recursion text data in compression table. To increase compression ratio, after construction of compression table, table data are properly packed as different bit string in accordance with a alphabet, Hangout, and pointer respectively. Therefore, proposed ELZW algorithm is superior to 1 byte LZW algorithm as 7.0125 percent and superior to 2 byte LZW algorithm as 11.725 percent. This paper represents a improved data Compression algorithm of mixed text file by 2 byte completion Hangout and 1 byte alphabet form. This document is an example of what your camera-ready manuscript to ITC-CSCC 2002 should look like. Authors are asked to conform to the directions reported in this document.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.499-507
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• 2016

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.786-792
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• 2004

A wavelet based ECG data compression has become an attractive and efficient method in many mobile tele-cardiology applications. But large data size required for high compression performance leads a serious delay. In this paper, new wavelet compression method with minimum delay is proposed. It is based on deciding the type and compression ratio(CR) of block organically according to the standard deviation of input ECG data with minimum block size. Compression performances of the proposed algorithm for different MIT ECG Records were analyzed comparing other ECG compression algorithm. In addition to the processing delay measurement, compression efficiency and reconstruction sensitivity to error were also evaluated via random noise simulation models. The results show that the proposed algorithm has both lower PRD than other algorithm on same CR and minimum time in the data acquisition, processing and transmission.