A review of the fractal image compression literature

  title={A review of the fractal image compression literature},
  author={Dietmar Saupe and Raouf Hamzaoui},
D ince the conception of fractal image compression by Michael F. Barnsley around 1987, the research literature on this topic has exper ienced a rapid growth. Following is a brief description of the major advances in the field and the largest, comprehensive bibliography published on this topic to date. While JPEG is becoming the industry standard for image compression technology, there is ongoing research in alternative methods. Currently there are at least two exciting new developments: wavelet… 

Figures and Tables from this paper

A Bibliography on Fractal Image Compression

This bibliography attempts to provide the scientiic community with a comprehensive list of references on fractal image compression with an updated version was published in the 1994 November issue of the ACM Computer Graphics journal.

Fractal Image Compression - An Introductory Overview

This paper has chosen the similarity to a particular variant of vector quantization as the most direct approach to fractal image compression and surveys some of the advanced concepts such as fast decoding, hybrid methods, and adaptive partitionings.

A Review on Fractal Image Compression

This paper presents method for generating fractal images using iterated function system, method to partition image for compressing image using fractal image compression technique and various quality measures in fractalimage compression.

Fractal image compression: a randomized approach

Generalized Square Isometries - An Improvement for Fractal Image Coding

Why effective non-linear transformations are not easy to find and a model based on conformai mappings in the geometric domain that are a natural extension of the affine model are proposed.

An image compression method based on fractal theory

This paper provides an elementary introduction to Fractal image compression, and surveys advanced concepts such as hybrid methods and adaptive portioning, and presents a heuristic searching algorithm to decrease encoding time.

Fast Fractal Image Compression Based on Domain-Range Pixel Value Difference

A new method to reduce the encoding time based on computing the pixel value difference of domain and range blocks is presented that improved in performance when compared to conventional fractal encoding.

Fractal Image Compression Project Report

This project implements an innovative method of partitioning an image, which reduces the computational complexity in the encoding step, and presents a comparison of this method against the quadtree method and against the JPEG standard.

Using adaptive contraction for fractal image coding based on local fractal dimension

  • A. ConciFelipe R. Aquino
  • Computer Science
    XII Brazilian Symposium on Computer Graphics and Image Processing (Cat. No.PR00481)
  • 1999
The use of the local complexity of the image domain blocks to reduce the number of pairs to be tested on this search by selecting an appropriate criterion for close match and the compression time can be shortened without image quality degradation.

Adaptive Gray Level Difference to Speed Up Fractal Image Compression

Experimental results on standard gray scale images show that the proposed method yields superior performance over conventional fractal encoding, while obtaining good fidelity and compression ratio for the decoded image.



Fractal image compression

Pruning of the transform space in block-based fractal image compression

A method for fractal image compression is presented which is an extension of A.E. Jacquin's block-based algorithm by allowing irregularly shaped fractal transformations and shows a large improvement in compression ratio overJacquin's system at the same signal-to-noise ratio.


A practical fractal image compression method using locally refined partition which is generated automatically and controlled by the values of gradients in images, similar to the ones by Barnsley and Hurd.

The use of fractal theory in a video compression system

The paper describes how fractal coding theory may be applied to compress video images using an image resampling sequencer (IRS) in a video compression system on a modular image processing system. It

Fractal-Based Image Compression

Abstract : A short review of the theory of iterated function systems (IFS), a thorough explanation of their implementation, and an example using computer code useful in developing encoded images are

Practical approach to fractal-based image compression

This paper presents a technique for image compression that is based on a very simple type of iterative fractal, used to decompose an image into bands containing information from different scales (spatial frequencies) and orientations, and uses the conditional probabilities between these different scale bands as the basis for a predictive coder.

Fractal approximation of image blocks

  • D. MonroF. Dudbridge
  • Computer Science
    [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing
  • 1992
The fidelity of the fractal method shows promise and its greater speed and simplicity compared to other fractal transforms suggest immediate applications such as interactive browsing of remote image archives or image representation in multimedia systems.

Least-squares block coding by fractal functions

The image coding scheme described in this chapter represents an attempt to achieve image compression using, as closely as possible, the “classical” theory of strictly self-similar fractals to provide a nearly optimal code for a single block, which may be obtained in linear time.

Fractal image compression for mass storage applications

Fractal encoding of images is complex and may require specialized hardware for real time applications, but the decoding process can be widely utilized because it is simple, fast, and suitable for software implementation.

Fractal image compression: A resolution independent representation for imagery

This fern, for example is encoded in less than 50 bytes and yet can be displayed at resolutions with increasing levels of detail appearing, demonstrating the power of the Fractal Transform.