Color Component Based Lossless Compression Method for  Satellite Images (CCBLC)

S Sanjith

S Sanjith Department of Computer Science and Engineering, Noorul Islam Centre for Higher Education, Kumarcoil, Kanyakumari District, Tamilnadu, India. Department of Computer Science, St. Alphonsa College of Arts and Science, Soosaipuram, Karinkal, Kanyakumari District, Tamilnadu – 629157, India

Keywords: RGB, Lossless compression, Color component, Satellite image, CCBLC

Abstract

In recent years, the development and demand of remote sensing images are increased rapidly, however the huge volume of satellite images are contributing to inadequate bandwidth of network and storage of memory device. Therefore, the hunt of data compression methods becomes more and more significant to reduce the data redundancy to save more hardware space and transmission bandwidth. This paper introduces a new lossless compression method which reduces the size of the image by performing on blocks that have the comparable color components because the importance of the color varies from block to block depending upon the image. The similar color blocks are identified by the histogram values of each color Red, Green and Blue. A threshold value is fixed and the high order color component blocks will be removed and mentioned in a frequency table. While using this method, we got a comparatively better compression ratio for satellite images than the existing compression methods.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

References

Singh A, Gahlawat M. Image compression and its various techniques. International Journal of Advanced Research in Computer Science and Software Engineering. 2013;3(6):650-654.

Nivedita SJ. Performance analysis of SVD and SPIHT algorithm for image compression application. International Journal of Advanced Research in Computer Science and Software Engineering. 2012;2(2).

Sanjith S, Ganesan R. A review on hyperspectral image compression. In2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT) 2014 Jul 10 (pp. 1159-1163). IEEE.

Sanjith S, Ganesan R, Isaac RS. Experimental analysis of compacted satellite image quality using different compression methods. Advanced Science, Engineering and Medicine. 2015;7(3):227-233.

Sanjith S, Ganesan R. Overview of Image Quality Metrics with Perspective to Satellite Image Compression. InInternational Journal of Engineering Research in Africa 2016 (Vol. 24, pp. 112-123). Trans Tech Publications Ltd.

Sanjith S, Ganeshan R. Evaluating the Quality of Compression in Very High Resolution Satellite Images Using Different Compression Methods. InInternational Journal of Engineering Research in Africa 2016 (Vol. 19, pp. 91-102). Trans Tech Publications Ltd.

Anuradha D, Bhuvaneswari S. A detailed review on the prominent compression methods used for reducing the data volume of big data. Annals of Data Science. 2016;3(1):47-62.

Miranda FP, MacDonald JA, Carr JR. Application of the semivariogram textural classifier (STC) for vegetation discrimination using SIR-B data of Borneo. International Journal of Remote Sensing. 1992;13(12):2349-2354.

Zhang L, Huang X, Huang B, Li P. A pixel shape index coupled with spectral information for classification of high spatial resolution remotely sensed imagery. IEEE Transactions on Geoscience and Remote Sensing. 2006;44(10):2950-2961.

Ryherd S, Woodcock C. Combining spectral and texture data in the segmentation of remotely sensed images. Photogrammetric engineering and remote sensing. 1996;62(2):181-194.