Comparative analysis of various Image compression techniques for Quasi Fractal lossless compression

  • Satyawati Magar Department of E&TC, D.V.V.P College of Engineering, Ahmednagar, Maharashtra, India.
  • Bhavani Sridharan Department of ECE, Sri Shakthi Institute of Engineering & Technology Coimbatore, Tamil Nadu, India.
Keywords: Adaptive Threshold, Oscillation Concept, BTC-SPIHT, Quasi Fractal, Morphological Filter, CR, PSNR


The most important Entity to be considered in Image Compression methods are Paek to signal noise ratio and Compression ratio. These two parameters are considered to judge the quality of any Image.and they a play vital role in any Image processing applications. Biomedical domain is one of the critical areas where more image datasets are involved for analysis and biomedical image compression is very, much essential. Basically, compression techniques are classified into lossless and lossy. As the name indicates, in the lossless technique the image is compressed without any loss of data. But in the lossy, some information may loss. Here both lossy & lossless techniques for an image compression are used. In this research different compression approaches of these two categories are discussed and brain images for compression techniques are highlighted. Both lossy and lossless techniques are implemented by studying it’s advantages and disadvantages. For this research two important quality parameters i.e. CR & PSNR are calculated. Here existing techniques DCT, DFT, DWT & Fractal are implemented and introduced new techniques i.e Oscillation Concept method, BTC-SPIHT & Hybrid technique using adaptive threshold & Quasi Fractal Algorithm.


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How to Cite
Magar, S., & Sridharan, B. (2020). Comparative analysis of various Image compression techniques for Quasi Fractal lossless compression. International Journal of Computer Communication and Informatics, 2(2), 30-45.

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