Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Rebuilding of Image Compression Algorithm Based on the DCT (discrete cosine transform)

이산코사인변환 기반 이미지 압축 알고리즘에 관한 재구성

  • Nam, Soo-Tai (Division of Information and Electronic Commerce (Institute of Convergence and Creativity), Wonkwang University) ;
  • Jin, Chan-Yong (Division of Information and Electronic Commerce (Institute of Convergence and Creativity), Wonkwang University)
  • Received : 2018.10.26
  • Accepted : 2018.11.21
  • Published : 2019.01.31
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Abstract

JPEG is a most widely used standard image compression technology. This research introduces the JPEG image compression algorithm and describes each step in the compression and decompression. Image compression is the application of data compression on digital images. The DCT (discrete cosine transform) is a technique for converting a time domain to a frequency domain. First, the image is divided into 8 by 8 pixel blocks. Second, working from top to bottom left to right, the DCT is applied to each block. Third, each block is compressed through quantization. Fourth, the matrix of compressed blocks that make up the image is stored in a greatly reduced amount of space. Finally if desired, the image is reconstructed through decompression, a process using IDCT (inverse discrete cosine transform). The purpose of this research is to review all the processes of image compression / decompression using the discrete cosine transform method.

JPEG은 가장 널리 사용되는 이미지 압축 표준 기술이다. 본 논문에서는 JPEG 이미지 압축 알고리즘을 소개하고 압축 및 압축 해제의 각 단계를 서술하고자 한다. 이미지 압축은 디지털 이미지를 데이터 압축을 적용하는 과정이다. 이산코사인변환은 시간 도메인에서 주파수 도메인으로 변환하는 기술이다. 먼저, 이미지는 8 by 8 픽셀 블록으로 분할하게 된다. 둘째, 위에서 아래로 왼쪽에서 오른쪽으로 진행하면서 DCT가 각각의 블록에 적용하게 된다. 셋째, 각 블록은 양자화를 통해 압축을 진행한다. 넷째, 이미지를 구성하는 압축된 블록의 행렬은 크게 줄어든 공간에 저장된다. 끝으로, 원하는 경우 이미지는 역이산코사인변환(IDCT)을 사용하는 프로세스인 압축 해제를 통해 재구성하게 된다. 본 연구에서는 이산코사인변환 기법을 이용해 이미지 압축/복원 및 재구성하는 것에 목적을 두고 있다.

Keywords

HOJBC0_2019_v23n1_84_f0001.png 이미지

Fig. 1 Zig-Zag scanning for encoding

Table. 1 Orthogonal matrix (T) using the discrete cosine transform

HOJBC0_2019_v23n1_84_t0001.png 이미지

Table. 2 Original image matrix (O) using the discrete cosine transform

HOJBC0_2019_v23n1_84_t0002.png 이미지

Table. 3 Matrix (M) using the discrete cosine transform

HOJBC0_2019_v23n1_84_t0003.png 이미지

Table. 4 Matrix (D) calculated by the transpose matrix

HOJBC0_2019_v23n1_84_t0004.png 이미지

Table. 5 Quantization Matrix (Q50) for compression

HOJBC0_2019_v23n1_84_t0005.png 이미지

Table. 6 Quantization Matrix (Q90) for compression

HOJBC0_2019_v23n1_84_t0006.png 이미지

Table. 7 Final matrix (C) completed compression

HOJBC0_2019_v23n1_84_t0007.png 이미지

Table. 8 Matrix (R) using the inverse discrete cosine transform

HOJBC0_2019_v23n1_84_t0008.png 이미지

Table. 9 Original image used for compression

HOJBC0_2019_v23n1_84_t0009.png 이미지

Table. 10 Image matrix reconstructed by the inverse discrete cosine transform

HOJBC0_2019_v23n1_84_t0010.png 이미지

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