Data compression through separation, transmission and encoded values of RGB

N bala Subrahmanyam

Abstract


This introduces a novel algorithm for image compression meaning to diminishdata parcel size, bringing about powerful transfer speed use duringdata transmissions. The algorithm alluded to as Differential Subtraction Chain (DSC) comprises of three stages. Initially, it isolates a image document to three matrices of RGB. Second, it registers component astute various qualities in every pixel among R and G matrices, and among G and B frameworks. Third, the various qualities are twofold encoded and changed to successive vectors all together fordata transmissions. In our MATLAB reproductions, the exhibition measure is compression proportion which is determined by [1-(packeddata size/uniquedata size)] 100%. The compression proportions yielded by our DSC tried with three benchmarking images of city, Lenna and Mandrill are 44.02%, 42.02% and 39.86%, individually.


References


C. M. Sadler and M. Martonosi, “Data compression algorithms for energy-constrained devices in delay tolerant matrices,” in Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, ser. SenSys ’06. New York, NY, USA: ACM, 2006, pp. 265– 278. [Online]. Available: http://doi.acm.org/10.1145/1182807.1182834 [2] D. Salomon, Data Compression: The Complete Reference. Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.

E. P. Capo-Chichi, H. Guyennet, and J. M. Friedt, “K-RLE: A new data compression algorithm for wireless sensor network,” in Sensor Technologies and Applications, 2009. SENSORCOMM ’09. Third International Conference on, June 2009, pp. 502–507.

S. Roy, S. C. Panja, and S. N. Patra, “DMBRLE: A lossless compression algorithm for solar irradiance data acquisition,” in Recent Trends indata Systems (ReTIS), 2015 IEEE 2nd International Conference on, July 2015, pp. 450–454.

F. Marcelloni and M. Vecchio, “A simple algorithm for data compression in wireless sensor matrices,” IEEE Communications Letters, vol. 12, no. 6, pp. 411–413, June 2008. [6] Z. Zou, Y. Bao, F. Deng, and H. Li, “An approach of reliable data transmission with random redundancy for wireless sensors in structural health monitoring,” IEEE Sensors Journal, vol. 15, no. 2, pp. 809–818, Feb 2015. [7] T. Szalapski and S. Madria, “On compressing data in wireless sensor matrices for energy efficiency and real time delivery,” Distributed and Parallel Databases, vol. 31, no. 2, pp. 151–182, Jun 2013. [Online]. Available: https://doi.org/10.1007/s10619-012-7111-5

G. Navarro and A. Ordez, “Compressing huffman models on large alphabets,” in 2013 Data Compression Conference, March 2013, pp. 381–390.

B. Nivedha, M. Priyadharshini, E. Thendral, and T. Deenadayalan, “Lossless image compression in cloud computing,” in 2017 International Conference on Technical Advancements in Computers and Communications (ICTACC), April 2017, pp. 112–115.

Y. Deigant, V. Akshat, H. Raunak, P. Pranjal, and J. Avi, “A proposed method for lossless image compression in nano-satellite systems,” in 2017 IEEE Aerospace Conference, March 2017, pp. 1–11.


Full Text: PDF [Full Text]

Refbacks

  • There are currently no refbacks.


Copyright © 2013, All rights reserved.| ijseat.com

Creative Commons License
International Journal of Science Engineering and Advance Technology is licensed under a Creative Commons Attribution 3.0 Unported License.Based on a work at IJSEat , Permissions beyond the scope of this license may be available at http://creativecommons.org/licenses/by/3.0/deed.en_GB.