Edge-directed prediction for lossless compression of natural images

  • Authors:

  • Xin Li;M. T. Orchard

  • Affiliations:

  • Sharp Labs. of America, Camas, WA;-

  • Venue:
  • IEEE Transactions on Image Processing
  • Year:
  • 2001

Quantified Score

Hi-index 0.01

Visualization

Abstract

This paper sheds light on the least-square (LS)-based adaptive prediction schemes for lossless compression of natural images. Our analysis shows that the superiority of the LS-based adaptation is due to its edge-directed property, which enables the predictor to adapt reasonably well from smooth regions to edge areas. Recognizing that LS-based adaptation improves the prediction mainly around the edge areas, we propose a novel approach to reduce its computational complexity with negligible performance sacrifice. The lossless image coder built upon the new prediction scheme has achieved noticeably better performance than the state-of-the-art coder CALIC with moderately increased computational complexity