By Topic

Entropy Coding via Parametric Source Model with Applications in Fast and Efficient Compression of Image and Video Data

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)
Minoo, K. ; Adv. Technol. Group, Motorola Inc., San Diego, CA ; Truong Nguyen

In this paper a framework is proposed for efficient entropy coding of data which can be represented by a parametric distribution model. Based on the proposed framework, an entropy coder achieves coding efficiency by estimating the parameters of the statistical model (for the coded data), either via maximum a posteriori (MAP) or Maximum Likelihood (ML) parameter estimation techniques. The problem of optimal entropy coding for transmission of a block of data x1,,x2,...xN , can be formulated by assuming that the data comes from a source with a parametric probability mass function (pmf) P(X1,X2,...XN;thetas) with parameter thetas (in general thetas is a vector). The parametric model assumption makes it possible to assign a probability to the event of observing x1,,x2,...xN, and use this probability for entropy coding of this data, only by conveying the parameter thetas.The impressive results from the simple parametric model, based on a geometric distribution of coded data for compression of natural images, are encouraging to further investigate the effect of more complicated data models such as Poisson distribution and mixture models.

Published in:

Data Compression Conference, 2009. DCC '09.

Date of Conference:

16-18 March 2009