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이중 밀도 웨이브렛 변환의 성능 향상을 위한 Quincunx 표본화 기법

Quincunx Sampling Method For Improvement of Double-Density Wavelet Transformation

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초록

This paper introduces the double-density discrete wavelet transform(DWT) using quincunx sampling, which is a DWT that combines the double-density DWT and quincunx sampling method, each of which has its own characteristics and advantages. The double-density DWT is an improvement upon the critically sampled DWT with important additional properties:Firstly, It employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. Secondly, the double-density DWT is overcomplete by a factor of two, and Finally, it is nearly shift-invariant. In two dimensions, this transform outperforms the standard DWT in terms of denoising; however, there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. A solution to this problem is a quincunx sampling method. The quincunx lattice is a sampling method in image processing. It treats the different directions more homogeneously than the separable two dimensional schemes. Proposed wavelet transformation can generate sub-images of multiple degrees rotated versions. Therefore, This method services good performance in image processing fields.

Abstract

This paper introduces the double-density discrete wavelet transform(DWT) using quincunx sampling, which is a DWT that combines the double-density DWT and quincunx sampling method, each of which has its own characteristics and advantages. The double-density DWT is an improvement upon the critically sampled DWT with important additional properties:Firstly, It employs one scaling function and two distinct wavelets, which are designed to be offset from one another by one half. Secondly, the double-density DWT is overcomplete by a factor of two, and Finally, it is nearly shift-invariant. In two dimensions, this transform outperforms the standard DWT in terms of denoising; however, there is room for improvement because not all of the wavelets are directional. That is, although the double-density DWT utilizes more wavelets, some lack a dominant spatial orientation, which prevents them from being able to isolate those directions. A solution to this problem is a quincunx sampling method. The quincunx lattice is a sampling method in image processing. It treats the different directions more homogeneously than the separable two dimensional schemes. Proposed wavelet transformation can generate sub-images of multiple degrees rotated versions. Therefore, This method services good performance in image processing fields.

참고문헌 (8)

  1. N. G. Kingsbury, "Complex wavelets for shift invariant analysis and filtering of signals", Appl. Comput. Harmon. Anal, 10(3), pp.234~253 (2001)
  2. Jingyu Yang, "Image Coding Using Dual-Tree Discrete Wavelet Transform", IEEE Trans. on Image Processing, 17(9), pp.1555~1569 (2008)
  3. I. W. Selesnick, "The double-density dual-tree DWT", IEEE Trans. on Signal Processing, 52(5), pp.1304~1314 (2004)
  4. W. Selesnick, "The double density DWT, In Wavelets in signal and Image Analysis: From Theory to Practice" (2001), Kluwer
  5. Kovacevic, "Wavelet families of increasing order in arbitrary dimensions", IEEE Trans. on Image Processing, 9(3), pp.480~496 (2000)
  6. 신종홍, "디지털 영상 처리를 위한 Quincunx 표본화가 사용된 이중 트리 이산 웨이브렛 변환", (사)디지털산업정보학회 논문지, 7(4), pp.119~131 (2011), (사)디지털산업정보학회
  7. Joonghee Lim, "Non-separable Image Processing Using Dual-Tree Complex Wavelet Transformation,", ITC-CSCC 2011, ~ (2011)
  8. 신종홍, "자유시점 TV를 위한 다시점 비디오의 계층적 깊이 영상 표현과 H.264 부호화", (사)디지털산업정보학회 논문지, 7(2), pp.91~100 (2011), (사)디지털산업정보학회

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