Image Compression using Discrete Cosine Transform, Block Truncation Coding and Gaussian Pyramidal Approach

Authors

  • Premal B. Nirpal New Model Degree College, Hingoli-431513 (MS)

Keywords:

Lossy Compression, DCT, BTC, GP, SAR image

Abstract

Image compression is the natural technology for handling the increased spatial resolutions of today's imaging sensors and evolving broadcast television standards. Image compression plays an important role in many important and diverse applications including conferencing, remote sensing, document and medical imaging, and the control of remotely piloted vehicles in military, space, and dangerous waste management applications. In this paper focus is given on the main Lossy Compression of SAR Image data. And at the end of this stage the different Quality Evaluation mechanisms are highlighted to measure the quality of resultant image and also to measure the efficiency of the algorithm. These quality measures are useful to check the quality of decompressed image and verify the competitiveness of the algorithm. This work covers the Discrete Cosine Transform (DCT), Block Truncation Coding (BTC) and Gaussian Pyramidal (GP) based compression techniques.

References

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Published

2015-04-30

How to Cite

[1]
N. Premal B, “Image Compression using Discrete Cosine Transform, Block Truncation Coding and Gaussian Pyramidal Approach”, Int. J. Comp. Sci. Eng., vol. 3, no. 4, pp. 21–25, Apr. 2015.

Issue

Vol. 3 No. 4 (2015): IJCSE April Edition

Section

Research Article

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