By Topic

Image and video compression algorithms based on recovery techniques using mean field annealing

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
T. Ozcelik ; Dept. of Electr. Eng. & Comput. Sci., Northwestern Univ., Evanston, IL, USA ; J. C. Brailean ; A. K. Katsaggelos

Image and video coding algorithms have found a number of applications ranging from video telephony on the public switched telephone networks (PSTN) to HDTV. However, as the bit rate is lowered, most of the existing techniques, as well as current standards, such as JPEG, H. 261, and MPEG-1 produce highly visible degradations in the reconstructed images primarily due to the information loss caused by the quantization process. In this paper, we propose an iterative technique to reduce the unwanted degradations, such as blocking and mosquito artifacts while keeping the necessary detail present in the original image. The proposed technique makes use of a priori information about the original image through a nonstationary Gauss-Markov model. Utilizing this model, a maximum a posteriori (MAP) estimate is obtained iteratively using mean field annealing. The fidelity to the data is preserved by projecting the image onto a constraint set defined by the quantizer at each iteration. The proposed solution represents an implementation of a paradigm we advocate, according to which the decoder is not simply undoing the operations performed by the encoder, but instead it solves an estimation problem based on the available bitstream and any prior knowledge about the source image. The performance of the proposed algorithm was tested on a JPEG, as well as on an H.261-type video codec. It is shown to be effective in removing the coding artifacts present in low bit rate compression

Published in:

Proceedings of the IEEE  (Volume:83 ,  Issue: 2 )