Open AccessComparative analysis of various Image compression techniques for Quasi Fractal lossless compression
Author(s) -
Satyawati S. Magar,
Bhavani Sridharan
Publication year - 2020
Publication title -
international journal of computer communication and informatics
Language(s) - English
Resource type - Journals
ISSN - 2582-2713
DOI - 10.34256/ijcci2024
Subject(s) - lossy compression , lossless compression , fractal transform , image compression , texture compression , fractal compression , data compression ratio , computer science , data compression , artificial intelligence , compression ratio , computer vision , peak signal to noise ratio , color cell compression , image processing , algorithm , image (mathematics) , physics , internal combustion engine , thermodynamics
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.