• 전자공학회논문지
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• 제49권10호
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• pp.135-141
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• 2012

LCD 오버드라이브에서 프레임 메모리 크기를 줄이는 방법으로, BTC 영상 압축이 널리 사용되고 있다. BTC 영상 압축에서압축률을 높이기 위해서는 비트맵 데이터를 압축하거나 양자화 데이터의 압축이 필요하다. 본 논문에서는 압축률을 높이기 위해서 CMBQ-BTC (CMBQ : compression method bitmap and quantization data) 알고리즘을 제안한다. 시뮬레이션으로 기존의 BTC 알고리즘과 PSNR 및 압축비율의 비교를 통해서, 제안한 알고리즘의 효율성을 확인하였다.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2006년도 Proceedings of ISRS 2006 PORSEC Volume I
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• pp.282-285
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• 2006

This paper analyzed the characteristics of the Lossy JPEG of the meteorological satellite image, and analyzed the quality of the Lossy JPEG compression, which is proper for the LRIT(Low Rate Information Transmission) to be serviced to the SDUS(Small-scale Data Utilization Station) system of the COMS(Communication, Oceans, Meteorological Satellite). Since COMS is to start running after 2008, we collected the data of the MTSAT-1R(Multi-functional Transport Satellite -1R) for analysis, and after forming the original image to be used to LRIT by each channel and time zone of the satellite image data, we set the different quality with the Lossy JPEG compression, and compressed the original data. For the characteristic analysis of the Lossy JPEG, we measured PSNR(Peak Signal to Noise Rate), compression rate and the time spent in compression following each quality of Lossy JPEG compression. As a result of the analysis of the satellite image data of the MTSAT-1R, the ideal quality of the Lossy JPEG compression was found to be 90% in the VIS Channel, 85% in the IR1 Channel, 80% in the IR2 Channel, 90% in the IR3 Channel and 90% in the IR4 Channel.

• International Journal of Internet, Broadcasting and Communication
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• 제16권2호
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• pp.22-30
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• 2024

Image compression-decompression methods have become increasingly crucial in modern times, facilitating the transfer of high-quality images while minimizing file size and internet traffic. Historically, early image compression relied on rudimentary codecs, aiming to compress and decompress data with minimal loss of image quality. Recently, a novel compression framework leveraging colorization techniques has emerged. These methods, originally developed for infusing grayscale images with color, have found application in image compression, leading to colorization-based coding. Within this framework, the encoder plays a crucial role in automatically extracting representative pixels-referred to as color seeds-and transmitting them to the decoder. The decoder, utilizing colorization methods, reconstructs color information for the remaining pixels based on the transmitted data. In this paper, we propose a novel approach to image compression, wherein we decompose the compression task into grayscale image compression and colorization tasks. Unlike conventional colorization-based coding, our method focuses on the colorization process rather than the extraction of color seeds. Moreover, we employ the Denoising Diffusion Null-Space Model (DDNM) for colorization, ensuring high-quality color restoration and contributing to superior compression rates. Experimental results demonstrate that our method achieves higher-quality decompressed images compared to standard JPEG and JPEG2000 compression schemes, particularly in high compression rate scenarios.

• 한국통신학회논문지
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• 제25권6B호
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• pp.1028-1040
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• 2000

Since image data has large data amount, proper image compression is necessary to transmit and store the data efficiently. Image compression brings about bit rate reduction but results in some artifacts. This artifacts are blocking artifacts, mosquito noise, which are observed in DCT based compression image, and ringing artifacts, which is perceived around the edges in wavelet based compression image. In this paper, we propose directional postprocessing technique which improved the decoded image quality using the fact that human vision is sensible to ringing artifacts around the edges of image. First we detect the edge direction in each block. Next we perform directional postprocessing according to detected edge direction. Proposed method is that the edge direction is block. Next performed directional postprocessing according to detected edge direction. If the correlation coefficients are equivalent to each directions, postprocessing is not performed. So, time of the postproces ing brings about shorten.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 학술대회 논문집 정보 및 제어부문
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• pp.358-360
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• 2004

The digital images contain a significant amount of redundancy and require a large amount of data for their storage and transmission. Therefore, the image compression is necessary to treat digital images efficiently. The goal of image compression is to reduce the number of bits required for their representation. The image compression can reduce the size of image data using contractive mapping of original image. Among the compression methods, the mapping is affine transformation to find the block(called range block) which is the most similar to the original image. In this paper, we applied the neural network(SOM) in encoding. In order to improve the performance of image compression, we intend to reduce the similarities and unnecesaries comparing with the originals in the codebook. In standard image coding, the affine transform is performed with eight isometries that used to approximate domain blocks to range blocks.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1999년도 추계종합학술대회 논문집
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• pp.491-494
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• 1999

Introduction of digital communication network such as Integrated Services Digital Networks(ISDN) and digital storage media have rapidly developed. Due to a large amount of image data, compression is the key techniques in still image and video using digital signal processing for transmitting and storing. Digital image compression provides solutions for various image applications that represent digital image requiring a large amount of data. In this paper, the proposed DWT(Discrete Wavelet Transform) filter bank is consisted of simple architecture, but it is efficiently designed that a user obtain a wanted compression rate as only input parameter. If it is implemented by FPGA chip, the designed encoder operates in 12MHz.

• 대한전자공학회논문지SD
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• 제49권3호
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• pp.30-36
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• 2012

BTC 압축은 간단하고 효율적인 압축 알고리즘으로 알려져 있다. 본 논문에서는, 컬러 이미지 압축을 위한 RMC-BTC 알고리즘(RMC : reduction method chrominace data)을 제안한다. RMC-BTC coding은 chrominace data를 축소시키기 위해서, 각 BTC 블록에서, chrominace data를 평균으로 표현하는 방법과, luminance 데이터 의 bit-map을 chrominace 데이터의 bit-map으로 활용하여 chrominace 데이터를 표현하는 방법을 사용하였다. 시뮬레에션 결과는 기존의 BTC 알고리즘의 PSNR과 압축비율의 비교를 통해서, 제안한 알고리즘의 효율성을 확인하였다.

• 한국인터넷방송통신학회논문지
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• 제12권5호
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• pp.39-54
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• 2012

오늘날 의료정보화 수준향상과 디지털 병원화의 흐름에 따라 PACS는 의료기관의 핵심 인프라 중 하나로 자리매김하였다. 이와 함께 생산되는 디지털 의료영상의 종류 및 의료영상 데이터가 양적으로 급증하고 있으며, 이는 의료영상 데이터의 효과적인 보관을 위한 의료영상 압축을 중요한 요소로 부각시킨다. 현재 의료영상에 관한 사실상의 표준인 DICOM 규격에서는 의료영상 압축을 위하여 무손실 압축기법인 RLE를 명시하고 있으나, 무손실 범용 압축기법인 RLE는 인체의 대칭성을 가지는 많은 의료영상에 적용하면 높은 압축율 기대하기 힘들다. 이 논문에서는 다양한 의료영상 중 대칭 특성을 크게 내포하는 뇌 CT 영상을 대상으로 하여 영상 내 관심영역을 검출하고 대칭특성에 따라 영상의 픽셀 값을 재코딩하는 전처리 하고 영상을 압축하는 기법을 제안한다. 실험에 의하면, 제안한 기법은 RLE 압축과 영상 내 관심영역을 검출하지 않고 압축할 때와 비교하여 높은 압축률을 보인다.

• 한국정보기술응용학회:학술대회논문집
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• 한국정보기술응용학회 2005년도 6th 2005 International Conference on Computers, Communications and System
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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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• 제19권2호
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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.