The likelihood term in restoration of transform-compressed imagery

Mark A. Robertson

doi:10.1117/12.525409

21 May 2004 The likelihood term in restoration of transform-compressed imagery

Mark A. Robertson

Proceedings Volume 5299, Computational Imaging II; (2004) https://doi.org/10.1117/12.525409
Event: Electronic Imaging 2004, 2004, San Jose, California, United States

Abstract

Compression of imagery by quantization of the data's transform coefficients introduces an error in the imagery upon decompression. When processing compressed imagery, often a likelihood term is used to provide a statistical description of how the observed data are related to the original noise-free data. This work derives the statistical relationship between compressed imagery and the original imagery, which is found to be embodied in a (in general) non-diagonal covariance matrix. Although the derivations are valid for transform coding in general, the work is motivated by considering examples for the specific cases of compression using the discrete cosine transform and the discrete wavelet transform. An example application of motion-compensated temporal filtering is provided to show how the presented likelihood term might be used in a restoration scenario.

Citation Download Citation

Mark A. Robertson "The likelihood term in restoration of transform-compressed imagery", Proc. SPIE 5299, Computational Imaging II, (21 May 2004); https://doi.org/10.1117/12.525409

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available