Spatially-varying IIR filter banks for image coding
Read Online

Spatially-varying IIR filter banks for image coding

  • 919 Want to read
  • ·
  • 74 Currently reading

Published by National Aeronautics and Space Administration, National Technical Information Service, distributor in [Washington, DC, Springfield, Va .
Written in English


  • Imaging systems.,
  • Electric filters, Digital.

Book details:

Edition Notes

Other titlesSpatially varying IIR filter banks for image coding.
StatementWilson C. Chung and Mark J.T. Smith.
Series[NASA contractor report] -- NASA CR-193727.
ContributionsSmith, Mark J. T., United States. National Aeronautics and Space Administration.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL14697475M

Download Spatially-varying IIR filter banks for image coding


Joshi R.L., Fischer T.R. () Subband Coding of Images Using Classification and Trellis Coded Quantization. In: Topiwala P.N. (eds) Wavelet Image and Video Compression. The International Series in Engineering and Computer Science, vol Author: Rajan L. Joshi, Thomas R. Fischer. Finally, the proposed IIR orthogonal wavelet filter banks are applied to the image compression, and then the coding performance of the proposed IIR Author: Umut Sezen. Spatially-varying IIR filter banks for image coding. filter banks to subband image coding is reported. The new filter bank is based on computationally efficient recursive polyphase. Directionality and Scalability in Subband Image and Video Compression. Authors; Authors and affiliations “Spatially-Varying IIR Filter Banks for Image Coding,” Proc. Int. Conf Taubman D., Chang E., Zakhor A. () Directionality and Scalability in Subband Image and Video Compression. In: Sanz J.L.C. (eds) Image Technology. Springer Cited by: 1.

Principles of Spatial Filters. For many applications, such as holography, spatial intensity variations in the laser beam are unacceptable. Our KT spatial filter system is ideal for producing a clean Gaussian beam. Figure 1: Spatial Filter System. The input Gaussian beam has spatially varying intensity "noise". On the other hand, for the intra-frame analysis and synthesis, one uses the highly efficient recursive filter banks. The proposed sub-band coding technique uses the base sub-band of the low-temporary subsequence in a simple detector of the scene moving areas. Information in other sub-bands is coded only in the areas related to significant. IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 22, NO. 12, DECEMBER Estimating Spatially Varying Defocus Blur From A Single Image Xiang Zhu, Member, IEEE, Scott Cohen, Member, IEEE, Stephen Schiller, Member, IEEE, and Peyman Milanfar, Fellow, IEEE Abstract—Estimating the amount of blur in a given image is important for computer vision . Image subband coding with adaptive filter banks John Hakon Husoy ; Sven Ole Aase Proc. SPIE , Visual Communications and Image Processing '92, pg 2 (1 November ); doi: /

Publications List: Ricardo L. de Queiroz. For copy or reprint of any of the ``Non-expansive pyramid for image coding using non-linear filter banks,'' IEEE Trans. on Image Processing, Vol. 7, pp. and R. Loce, ´´Spatially varying gray component replacement for image watermarking,``, Proc. IEEE Intl. Conf. on Image. Lecture Image Enhancement and Spatial Filtering Harvey Rhody Chester F. Carlson Center for Imaging Science Rochester Institute of Technology [email protected] Septem Abstract Applications of point processing to image segmentation by global and regional segmentation are constructed and demonstrated. An adaptive threshold algorithm is File Size: 7MB. Article. Zhang, G and Kingsbury, N () Variational Bayesian image restoration with group-sparse modeling of wavelet coefficients. DIGITAL SIGNAL PROCESSING, pp. ISSN Peters, N and Oppenheimer, C and Kyle, P and Kingsbury, N () Decadal persistence of cycles in lava lake motion at Erebus volcano, Antarctica. Earth and Planetary . The advent of digital video opens up a number of opportunities for interactive video communications and services, which require various amounts of digital video processing. The chapter concludes with an overview of the digital video processing problems that will be addressed in this book. v(xl,xz,q = [Vl(x1,+2,t),V2(21,C2,t)lT.