Segmentation based reversible image compression

Krishna Ratakonda, Narendra Ahuja

Research output: Contribution to conferencePaperpeer-review

Abstract

Reversible compression of images has been the topic of considerable research, as it finds applications in many fields in which the deviation of the reproduced image from the original image is intolerable, however small be the deviation. This paper is concerned with the problem of reducing spatial redundancies in gray scale images, thus providing effective lossless compression, using segmentation information. We will present new edge models that deal effectively with two issues that make such models normally unsuitable for compression applications: local applicability and large number of parameters needed for representation. Segmentation information is provided by a recent transform, which we found to possess qualities making it especially suitable for compression. The final residual image is obtained using autocorrelation-based 2-D linear prediction. Different implementations providing lossless compression are presented along with results over a number of common test images. Results show that the proposed approach can be used to yield robust lossless compression, while providing consistently and significantly better results than the best possible JPEG lossless coder.

Original languageEnglish (US)
Pages81-84
Number of pages4
StatePublished - Dec 1 1996
EventProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3) - Lausanne, Switz
Duration: Sep 16 1996Sep 19 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Image Processing, ICIP'96. Part 2 (of 3)
CityLausanne, Switz
Period9/16/969/19/96

ASJC Scopus subject areas

  • Hardware and Architecture
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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