By Topic

Depth image lossless compression using mixtures of local predictors inside variability constrained regions

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

2 Author(s)
Schiopu, I. ; Dept. of Signal Process., Tampere Univ. of Technol., Tampere, Finland ; Tabus, I.

This paper studies the lossless compression of depth images realized by first transmitting contours of suitably chosen regions and subsequently performing predictive coding inside each region and transmitting the prediction residuals. For the large constant depth regions only the contour needs to be transmitted along with the value of the depth inside each region, while for the rest of the image we find suitable regions where the local variation of the depth level from one pixel to another is constrained above. The nonlinear predictors used for each region combine the results of several linear predictors, each fitting optimally a subset of pixels belonging to the local neighborhood. Overall the obtained results exceed by a wide margin the performance of standard image compression algorithms.

Published in:

Communications Control and Signal Processing (ISCCSP), 2012 5th International Symposium on

Date of Conference:

2-4 May 2012