By Topic

Progressive Significance Map and Its Application to Error-Resilient Image Transmission

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)
Yang Hu ; Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA ; William A. Pearlman ; Xin Li

Set partition coding (SPC) has shown tremendous success in image compression. Despite its popularity, the lack of error resilience remains a significant challenge to the transmission of images in error-prone environments. In this paper, we propose a novel data representation called the progressive significance map (prog-sig-map) for error-resilient SPC. It structures the significance map (sig-map) into two parts: a high-level summation sig-map and a low-level complementary sig-map (comp-sig-map). Such a structured representation of the sig-map allows us to improve its error-resilient property at the price of only a slight sacrifice in compression efficiency. For example, we have found that a fixed-length coding of the comp-sig-map in the prog-sig-map renders 64% of the coded bitstream insensitive to bit errors, compared with 40% with that of the conventional sig-map. Simulation results have shown that the prog-sig-map can achieve highly competitive rate-distortion performance for binary symmetric channels while maintaining low computational complexity. Moreover, we note that prog-sig-map is complementary to existing independent packetization and channel-coding-based error-resilient approaches and readily lends itself to other source coding applications such as distributed video coding.

Published in:

IEEE Transactions on Image Processing  (Volume:21 ,  Issue: 7 )