Optimizing prediction gain in axial symmetric scans

N. Memon, D. Neuhoff, S. Shende

Research output: Contribution to conferencePaperpeer-review


Though most lossless image coding techniques use a raster scan to order the pixels for context-based predictive coding, other scans, such as the Hilbert or Peano scan, have been proposed as alternatives with potentially better performance. However, a general understanding of the merits of different scans has been lacking. In previous work, the authors had presented a framework in which the effect of pixel scan order on lossless compression can be quantitatively analyzed, so that comparisons of different scans can be made. Assuming a quantized-Gaussian and isotropic image model with contexts consisting of previously scanned adjacent pixels in a distance constrained neighborhood, it was found that the raster scan is better than the Hilbert scan. In this paper we further develop our arguments and show that for a large class of scans, which we call axial symmetric scans, the raster scan is indeed optimal. We would like to note that many common scans including the Hilbert scan fall under the class of axial symmetric scans.

Original languageEnglish (US)
Number of pages4
StatePublished - 2000
Externally publishedYes
EventInternational Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada
Duration: Sep 10 2000Sep 13 2000


OtherInternational Conference on Image Processing (ICIP 2000)
CityVancouver, BC

All Science Journal Classification (ASJC) codes

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


Dive into the research topics of 'Optimizing prediction gain in axial symmetric scans'. Together they form a unique fingerprint.

Cite this