A proposed method for lossless image compression in nano-satellite systems

Yadava Deigant, Vora Akshat, Hosangadi Raunak, Paliwal Pranjal, Jain Avi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

This paper introduces a lossless image compression algorithm aimed at compressing thermal images captured, stored and transmitted by a Nano-satellite system. This algorithm is applied specifically to thermal grey scale images. The compression method used tries to achieve a balance between the compression ratio achieved and the computational power consumed onboard a nanosatellite. For this purpose, the proposed method consists of various stages through which the image is passed. The paper describes the compression efficiency that is achieved using each stage and the subsequent hardware implementation to achieve desired data transfer rates. The proposed algorithm is easily implementable, tries to minimize the memory required onboard the satellite to judiciously use hardware resources, which are important factors for real time image compression on Nano-satellite systems. The algorithm is applied on a few images and the compression ratio obtained with each image is compared.

Original languageEnglish
Title of host publication2017 IEEE Aerospace Conference
PublisherIEEE Computer Society
ISBN (Electronic)9781509016136
DOIs
Publication statusPublished - 07-06-2017
Externally publishedYes
Event2017 IEEE Aerospace Conference, AERO 2017 - Big Sky, United States
Duration: 04-03-201711-03-2017

Conference

Conference2017 IEEE Aerospace Conference, AERO 2017
CountryUnited States
CityBig Sky
Period04-03-1711-03-17

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering
  • Space and Planetary Science

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  • Cite this

    Deigant, Y., Akshat, V., Raunak, H., Pranjal, P., & Avi, J. (2017). A proposed method for lossless image compression in nano-satellite systems. In 2017 IEEE Aerospace Conference [7943682] IEEE Computer Society. https://doi.org/10.1109/AERO.2017.7943682