This document presents a new image compression technique based on polynomial interpolation. It describes converting image pixels into polynomial factors through interpolation. The technique proposes sorting subimages to improve compression ratios and peak-to-noise ratios compared to other methods. Preliminary results show the polynomial interpolation approach provides promising enhancements for image compression by reducing file sizes while maintaining good quality in reconstructed images.

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Fractal Image Compression based on Polynomial Interpolation
Abstract
As of today, image compression is still being enhanced bringing new mathematical
methods in this interesting field aiming to reduce images' sizes as well as maintaining
good level of their corresponding reconstructed images. In this paper, we present a new
image compression technique based on polynomial interpolation. It is based on the
interpolation of image pixels and converting them into polynomial factors. Along our
research, we have also proposed some enhancements to the original approach such as
subimage sorting in order to reach better compression measures. Preliminary results
show a promising improvement in terms of compression ratio and peak to noise ratio
compared to peer techniques.