Open AccessPerformance Evaluation of Data Compression Systems Applied to Satellite Imagery
Author(s) -
Lilian N. Faria,
Leila María García Fonseca,
Max H. M. Costa
Publication year - 2012
Publication title -
journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.318
H-Index - 25
eISSN - 2090-0155
pISSN - 2090-0147
DOI - 10.1155/2012/471857
Subject(s) - lossless compression , lossy compression , image compression , data compression , jpeg , computer science , multispectral image , jpeg 2000 , compression ratio , data compression ratio , image quality , computer vision , artificial intelligence , remote sensing , image processing , image (mathematics) , engineering , geography , internal combustion engine , automotive engineering
Onboard image compression systems reduce the data storage and downlink bandwidth requirements in space missions. This paper presents an overview and evaluation of some compression algorithms suitable for remote sensing applications. Prediction-based compression systems, such as DPCM and JPEG-LS, and transform-based compression systems, such as CCSDS-IDC and JPEG-XR, were tested over twenty multispectral (5-band) images from CCD optical sensor of the CBERS-2B satellite. Performance evaluation of these algorithms was conducted using both quantitative rate-distortion measurements and subjective image quality analysis. The PSNR, MSSIM, and compression ratio results plotted in charts and the SSIM maps are used for comparison of quantitative performance. Broadly speaking, the lossless JPEG-LS outperforms other lossless compression schemes, and, for lossy compression, JPEG-XR can provide lower bit rate and better tradeoff between compression ratio and image quality
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom