Personal Satellite Services. Second International ICST Confernce, PSATS 2010, Rome, Italy, February 2010 Revised Selected Papers

Research Article

Comparative Analysis of Image Compression Algorithms for Deep Space Communications Systems

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  • @INPROCEEDINGS{10.1007/978-3-642-13618-4_5,
        author={Igor Bisio and Fabio Lavagetto and Mario Marchese},
        title={Comparative Analysis of Image Compression Algorithms for Deep Space Communications Systems},
        proceedings={Personal Satellite Services. Second International ICST Confernce, PSATS 2010, Rome, Italy, February 2010 Revised Selected Papers},
        proceedings_a={PSATS},
        year={2012},
        month={5},
        keywords={Deep Space Communications Systems Image Compression JPEG2000 CCSDS Compression PSNR Compression Time},
        doi={10.1007/978-3-642-13618-4_5}
    }
    
  • Igor Bisio
    Fabio Lavagetto
    Mario Marchese
    Year: 2012
    Comparative Analysis of Image Compression Algorithms for Deep Space Communications Systems
    PSATS
    Springer
    DOI: 10.1007/978-3-642-13618-4_5
Igor Bisio1,*, Fabio Lavagetto1,*, Mario Marchese1,*
  • 1: University of Genoa
*Contact email: igor.bisio@unige.it, fabio.lavagetto@unige.it, mario.marchese@unige.it

Abstract

In deep communications systems bandwidth availability, storage and computational capacity play a crucial role and represent precious, as well as limited, communications resources. Starting from this consideration, high efficient image compression coding algorithms may represent a key solution to optimize the resources employment. In this paper two possible approaches have been considered: JPEG2000 and CCSDS Image Compression, which is specifically designed for satellite and deep space communications. In more details, two coders have been compared in terms of performance: , which is an implementation of the JPEG2000 standards, and the BPE, which is based on the CCSDS recommendations. The proposed comparison takes into account both the quality of the compressed images, by evaluating the Peak Signal to Noise Ratio, and the time needed to compress the images: the Compression Time. The latter parameters, which concerns the computational complexity of the compression algorithm, is very interesting for deep space systems because of their limited computational and energy resources.