By Topic

Low bit rate image coding in the scale space

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
$31 $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

1 Author(s)
Xin Li ; Sharp Labs. of America, Camas, WA, USA

Scale-space representation has been extensively studied in the computer vision community for analyzing image structures at different scales. This paper borrows and develops useful mathematical tools from scale-space theory to facilitate the task of image compression. Instead of compressing the original image directly, we propose to compress its scale-space representation obtained by the forward diffusion with a Gaussian kernel at the chosen scale. The major contribution of this work is a novel solution to the ill-posed inverse diffusion problem. We analytically derive a nonlinear filter to deblur Gaussian blurring for 1D ideal step edges. The generalized 2D edge enhancing filter only requires the knowledge of local minimum/maximum and preserves the geometric constraint of edges. When combined with a standard wavelet-based image coder, the forward and inverse diffusion can be viewed as a pair of pre-processing and post-processing stages used to select and preserve important image features at the given bit rate. Experiment results have shown that the proposed diffusion-based techniques can dramatically improve the visual quality of reconstructed images at low bit rate (below 0.25bpp).

Published in:

Data Compression Conference, 2002. Proceedings. DCC 2002

Date of Conference: