Implementation of Multi-Linear Gain Prior to Image Compression System in Remote Sensing Electro-Optical Payloads

Автор: Ashok Kumar, Rajiv Kumaran

Журнал: International Journal of Image, Graphics and Signal Processing(IJIGSP) @ijigsp

Статья в выпуске: 3 vol.7, 2015 года.

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Future high resolution instrument planned by ISRO for space remote sensing will lead to higher data rates because of increase in resolution and dynamic range. Hence, image compression implementation becomes mandatory. Presently designed compression technique does not take account of imaging system noise like photon noise etc. This ignorance affects compression system performance. As a solution, this paper proposes MLG (Multi Linear Gain) operation prior to main compression system. With digital MLG operation, captured image can be optimally adjusted to systems noise. Proposed MLG operation improves compression ratio. Simulation results show 15-30% improvement in lossless compression ratio. However this improvement depends on MLG gains and corner points which can be driven by system SNR plot. MLG operation also helps in improving SNR at lower radiance input, when lossy JPEG2000 compression is used as main compression. Up to 1-6 dB SNR improvement is observed in simulations. Proposed MLG implementation is of very low complexity and planned to be used in future missions.

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Multi Linear Gain (MLG), image compression, JPEG, SNR, photon noise

Короткий адрес: https://sciup.org/15013538

IDR: 15013538

Список литературы Implementation of Multi-Linear Gain Prior to Image Compression System in Remote Sensing Electro-Optical Payloads

  • Ashok Kumar, Rajiv Kumaran, "Improvement in DPCM image comrpession technique", itSIP-2013, 18-19 Oct-2013, Mumbai-India, pp 280-284.
  • Ashok Kumar, Rajiv Kumaran, "A low complex ADPCM image compression technique with higher compression ratio", IJCET, Vol 4 Issue 6, Nov-Dec (2013), pp 367-377.
  • Guoxia Yu, Tanya Vladimirova, "Image Compression systems on board satellites", Acta Astronautica, vol 64, pp 988-1005, 2009.
  • Majid Rabbani, "Digital Image Compression Techniques",Eastman Kodak Company, Volume TT 7, Spie Optical Engineering Press, 1995.
  • Theuwissen A., "Solid-State Imaging with Charge-Coupled Devices", Kluwer Academic Publishers, 1995.
  • Filippov, A., "How many bits are really needed in the image pixels?". http://www.linuxdevices.com/articles/AT9913651997.html.
  • Gregor Fischer, "A Survey on Lossy Compression of DSC Raw Data", SPIE proceeding Electronic Imaging 2008.
  • F. Ebrahimi, M. Chamik, S. Winkler, "JPEG vs. JPEG2000: An objective comparison of image encoding quality", Proc. SPIE Applications of Digital Image Processing, vol. 5558, 300-308, 2004.
  • U. Steingrimsson, K. Simon, "Quality Assessment of the JPEG 2000 Compression Standard", Proc. of the CGIV 2004 Aachen, 337-342, Germany, April 2004 , 2004.
  • U. Steingrimsson, K. Simon, "Perceptive Quality Estimation: JPEG 2000 versus JPEG", Journal of Imaging Science and Technology, (47), 572-603, 2003.
  • M.J. Weinberger, G. Seroussi, G. Sapiro, "The LOCO-I lossless image compression algorithm: principles and standardization into JPEG-LS", IEEE Transactions on Image Processing 9 (8) (2000) 1309–1324.
  • W.B. Pennebaker, J.L. Mitchell, "JPEG Still Image Data Compression Standard", Chapman & Hall, New York, 1993.
  • Ashok Kumar, "Low complex ADPCM image compression technique", itSIP-2013, 18-19 Oct-2013, Mumbai-India, pp 276-279.
  • Deviprasad: "Indian Remote Sensing Satellites- Resourcesat2 Mission Status", India Civil Commercial Imagery Evaluation Workshop, March 17, 2010.
  • User Manual, Kakadu software v6.1.
  • Rafel C Gonzalez, "Digital Image Processing using MATLAB".
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