By Topic

Lossless Compression of Colour Video Sequence using Optimal Prediction Theory - Octopus

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)
Andriani, S. ; Dept. of Inf. Eng., Padova Univ. ; Calvagno, G.

Summary form only given. In this paper we present a lossless compression algorithm for colour video sequence which exploits the spatial, the spectral and the temporal correlations of a colour video sequence in the RGB colour space using the well-known optimal prediction theory. The main idea is to construct the optimal prediction coefficients estimating an autocorrelation matrix which exploits all these correlations. No colour transformation or motion compensation are applied because reversible colour transformations are not able to fully decorrelate the three bands of each frame, and motion compensation remarkably increases the complexity of the updating step of the autocorrelation matrix estimate. Furthermore, the fact that our algorithm is not based on motion compensation lead it to be robust to scene changes. The prediction errors are then coded using a context-based Golomb-Rice coder, with bias cancellation, but without run-length coding. To construct the contexts, the prediction errors are then modeled using an estimate of their local variance. This estimate considers all the previous prediction errors, using a forgetting factor to improve the adaptability of the proposed algorithm. The quantized estimated variance values are used as contexts for the Golomb-Rice coder, and, among others, we considered the following solutions: - 12 contexts estimated by sampling the standard deviation with a quantization step Delta = 1, and a saturation threshold equal to 12, [delta12]; - 128 contexts estimated by sampling the standard deviation with Delta = 1/3 and a saturation threshold equal to 128/3, [delta128]. The obtained coding results are presented for the proposed algorithm compared to JPEG-LS (without using any colour transformation), and JPEG2000 (in lossless mode, using the reversible colour YDbDr transform, and the 5/3 DWT). The results show an improvement of about 1.5 bpp and 0.65 bpp if compared with JPEG-LS coder, and- JPEG2000, respectively

Published in:

Data Compression Conference, 2007. DCC '07

Date of Conference:

27-29 March 2007