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https://github.com/vidiptvashist/fractal-image-compression-fic-banach-fixed-point-theorem


https://github.com/vidiptvashist/fractal-image-compression-fic-banach-fixed-point-theorem

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README 

        
# Fractal-Image-Compression-FIC-Banach-Fixed-Point-Theorem

With the advance of the information age the need for mass information

storage and fast communication links grows. Storing images in less

memory leads to a direct reduction in storage cost and faster data

transmissions. These facts justify the efforts, of private companies

and universities, on new image compression algorithms. Images are

stored on computers as collections of bits (a bit is a binary unit of

information which can answer “yes” or “no” questions) representing

pixels or points forming the picture elements. Since the human eye can

process large amounts of information (some 8 million bits), many pixels

are required to store moderate quality images. These bits provide the

“yes” and “no” answers to the 8 million questions that determine the

image. Most data contains some amount of redundancy, which can

sometimes be removed for storage and replaced for recovery, but this

redundancy does not lead to high compression ratios. An image can be

changed in many ways that are either not detectable by the human eye

or do not contribute to the degradation of the image. The standard

methods of image compression come in several varieties. The current

most popular method relies on eliminating high frequency components

of the signal by storing only the low frequency components (Discrete

Cosine Transform Algorithm). This method is used on JPEG (still

images), MPEG (motion video images), H.261 (Video Telephony on

ISDN lines), and H.263 (Video Telephony on PSTN lines) compression

algorithms. Fractal Compression was first promoted by M.Barnsley

The purpose of the image compression is to remove these redundancies

and thereby to reduce the size of the image to represent it for the

suitable application. Broadly, all the image compression techniques

are categorized as lossless and lossy. In lossless image compression,

an image is reversible and in lossy image compression techniques it is

irreversible. There are many popular image compression techniques