Journal of IKEEE (전기전자학회논문지)

Institute of Korean Electrical and Electronics Engineers (한국전기전자학회)
권호 표지
DOI QR코드

DOI QR Code

A Study on Applications of Wavelet Bases for Efficient Image Compression

효과적인 영상 압축을 위한 웨이브렛 기저들의 응용에 관한 연구

  • Jee, Innho (Dept. of Computer and Information Communications Engineering, Hongik University)
  • Received : 2017.02.14
  • Accepted : 2017.03.15
  • Published : 2017.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Image compression is now essential for applications such as transmission and storage in data bases. For video and digital image applications the use of long tap filters, while not providing any significant coding gain, may increase the hardware complexity. We use a wavelet transform in order to obtain a set of bi-orthogonal sub-classes of images; First, the design of short kernel symmetric analysis is presented in 1-dimensional case. Second, the original image is decomposed at different scales using a subband filter banks. Third, this paper is presented a technique for obtaining 2-dimensional bi-orthogonal filters using McClellan transform. It is shown that suggested wavelet bases is well used on wavelet transform for image compression. From performance comparison of bi-orthogonal filter, we actually use filters close to ortho-normal filters on application of wavelet bases to image analysis.

영상 압축은 데이터베이스에서 전송과 저장의 응용에 매우 중요한 분야이다. 비디오나 디지털 영상 응용에서 긴 탭의 필터를 사용하면 의미 있는 정도의 코딩이득은 얻지만 하드웨어의 복잡도를 증가시킨다. 우리는 한 쌍의 쌍직교 성질의 부 분할의 영상을 얻기 위하여 웨이브렛 변환을 사용한다. 첫째, 짧고 주요한 대칭 분석의 구현을 1차원 경우에 제시하였다. 둘째, 원래의 영상이 부대역 필터뱅크를 사용하여 다른 스케일로 분해되었다. 셋째, 본 논문에서 McClellan 변환을 사용하여 2차원의 쌍직교 필터를 얻는 기법을 제시하였다. 제시하는 웨이브렛 기저들이 영상압축에 사용되는 웨이브렛 변환에 효과적으로 사용될 수 있음을 보였다. 쌍직교 필터들의 성능 비교표로부터 웨이브렛 기저의 영상에 대한 응용에서는 우리는 실제적으로 ortho-normal 필터에 근사한 필터를 사용한다.

Keywords

References

  1. A. N. Akansu and R. A. Haddad, Multi-resolution Signal Decomposition: Transforms, Subbands, and Wavelets. Academic Press, 1992.
  2. A. N. Akansu, R. A. Haddad and H. Calgar, "The binomial QMF-wavelet transform for multi-resolution signal decomposition," IEEE Trans. on Signal Processing, vol. 41, no 1, pp. 13-36, Jan. 1993. https://doi.org/10.1109/TSP.1993.193123
  3. D. Estban and C. Galand, "Application of Quadrature mirror filters to split band voice coding scheme," Int'l., IEEE Conf. on ASSP, pp. 191-195, 1977. DOI: 10.1109/ICASSP.1977.1170341
  4. M. Vetterli, "Multi-dimensional subband coding: Some Theory and Algorithms," Signal Processing, IEEE, pp. 97-112, June 1984. DOI:10.1016/0165-1684(84)90012-4
  5. M. Antonini, M. Barlaud, P. Mathieau and I. Daubechies, "Image coding using wavelet transform." IEEE Trans. on Image Proc., vol. 1, no 2, pp. 205-220, April 1992. DOI: 10.1109/83.136597
  6. S. Lyu and E. P. Simoncelli, "Statistical modeling of images with fields of Gaussian scale mixtures, in Adv. Neural Information Processing Systems, vol. 19, pp. 100-105, 2007.
  7. W. A. Pearlman and A. Said, Digital Signal Compression Principles and Practice, Cambridge University Press, 2011.
  8. I. Daubechies, "Ortho-normal bases of compactly supported wavelet, II Variations on a theme," ATT Bell lab., Tech. Rep. TM 11217-891116-17, 1990. DOI:10.1137/0524031
  9. P. Burt and E. Adelson, "The laplacian pyramid as a compact image code," IEEE Trans. Commun., vol. 31, pp. 482-540. April 1983.DOI: 10.1109/TCOM.1983.1095851
  10. S. Mallet, "A theory for multi-resolution signal decomposition: The wavelet representation," IEEE Trans Pattern Anal. Mach. Intel., vol. 11, July 1989. DOI: 10.1109/34.192463
  11. D. Lee Fugal, Conceptual Wavelets in Digital Signal Processing, Space and Signal Technical Publishing, 2009.
  12. Dong-Min Woo,"Quantitative assessment of 3D reconstruction procedure using stereo matching," j.inst.Korean.electr.electron.eng, vol. 17, no 1, pp. 1-9, Mar. 2013. DOI : 10.7471/ikeee.2013.17.1.001