Prediction Error Coder: a fast lossless compression method for satellite noisy data

Alberto G. Villafranca; Jordi Portell; Enrique García-Berro

doi:10.1117/1.JRS.7.074593

9 August 2013 Prediction Error Coder: a fast lossless compression method for satellite noisy data

Alberto G. Villafranca, Jordi Portell, Enrique García-Berro

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 074593 (August 2013). https://doi.org/10.1117/1.JRS.7.074593

Abstract

Lossless compression is often required for downloading data of scientific payloads in space missions. The consultative committee for space data systems (CCSDS) 121.0 recommendation on lossless data compression is a “de-facto” standard, and it has been used in several missions so far owing to the reasonable compression ratios achieved with low processing requirements. Although the Rice coder used by this standard is optimal when dealing with noiseless Laplacian-distributed data, its performance rapidly degrades when noisy data are compressed or when there is a significant fraction of outliers in the input data. An alternative to this is PEC, the Prediction Error Coder, which is the core of the FAPEC adaptive entropy coder. We describe, analyze and test PEC on real and simulated data, revealing its key role in the excellent outlier resiliency of FAPEC. PEC is a fast and noise-resilient semi-adaptive entropy coder that can achieve better performances than the CCSDS standard in the presence of noise or when the input data contains a sizable fraction of outliers, while requiring very low processing resources.

Citation Download Citation

Alberto G. Villafranca, Jordi Portell, and Enrique García-Berro "Prediction Error Coder: a fast lossless compression method for satellite noisy data," Journal of Applied Remote Sensing 7(1), 074593 (9 August 2013). https://doi.org/10.1117/1.JRS.7.074593

Published: 9 August 2013

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