Earticle

초록

영어

For the last two decades the wavelet theory has been studied by many researchers to answer the demand of better and more appropriate functions to represent signals than the one offered by the Fourier analysis. Wavelets study each component of the signal on different resolutions and scales. One of the most attractive features that wavelet transformations provide is that their capability to analyze the signals which contain sharp spikes and discontinuities. Early implementations of the wavelet transform were based on filters’ convolution algorithms. This approach requires a huge amount of computational resources. In fact at each resolution, the algorithm requires the convolution of the filters used with the approximation image. Relatively recent approaches are using the Lifting Schemes (LS). In this paper we provide the taxonomy and current state of the art in Lifting Schemes (LS).

목차

Abstract
 1. Introduction
 2. Taxonomy of Lifting Scheme
  2.1. Designing Wavelets
  2.2. Wavelet Transformation: Desecrate Approach
  2.3. Lifting
 3. Sources of Compression
  3.1. Image Compression
  3.2. Video Compression
  3.3. Data Compression
 4. Current State of the Art
 5. Conclusion
 References

키워드

저자

• S. Nagaraja Rao [ Professor of Electronics and Communication Engineering, G.Pullaiah College of Engineering and Technology, Kurnool, A.P., India ]
• Dr. M. N. Giriprasad [ Professor & Head of Electronics and Communication Engineering, J.N.T.U.College of Engineering, Pulivandula, A.P., India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605