A survey of parallel algorithms for fractal image compression

This paper presents a short survey of the key research work that has been undertaken in the application of parallel algorithms for Fractal image compression. The interest in fractal image compression techniques stems from their ability to achieve high compression ratios whilst maintaining a very high quality in the reconstructed image. The main drawback of this compression method is the very high computational cost that is associated with the encoding phase. Consequently, there has been significant interest in exploiting parallel computing architectures in order to speed up this phase, whilst still maintaining the advantageous features of the approach. This paper presents a brief introduction to fractal image compression, including the iterated function system theory upon which it is based, and then reviews the different techniques that have been, and can be, applied in order to parallelize the compression algorithm

Fast Search Approaches for Fractal Image Coding: Review of Contemporary Literature

Fractal Image Compression FIC as a model was conceptualized in the 1989 In furtherance there are numerous models that has been developed in the process Existence of fractals were initially observed and depicted in the Iterated Function System IFS and the IFS solutions were used for encoding images The process of IFS pertaining to any image constitutes much lesser space for recording than the actual image which has led to the development of representation the image using IFS form and how the image compression systems has taken shape It is very important that the time consumed for encoding has to be addressed for achieving optimal compression conditions and predominantly the inputs that are shared in the solutions proposed in the study depict the fact that despite of certain developments that has taken place still there are potential chances of scope for improvement From the review of exhaustive range of models that are depicted in the model it is evident that over period of time numerous advancements have taken place in the FCI model and is adapted at image compression in varied levels This study focus on the existing range of literature on FCI and the insights of various models has been depicted in this stud

Self-similarity and wavelet forms for the compression of still image and video data

This thesis is concerned with the methods used to reduce the data volume required to represent still images and video sequences. The number of disparate still image and video coding methods increases almost daily. Recently, two new strategies have emerged and have stimulated widespread research. These are the fractal method and the wavelet transform. In this thesis, it will be argued that the two methods share a common principle: that of self-similarity. The two will be related concretely via an image coding algorithm which combines the two, normally disparate, strategies. The wavelet transform is an orientation selective transform. It will be shown that the selectivity of the conventional transform is not sufficient to allow exploitation of self-similarity while keeping computational cost low. To address this, a new wavelet transform is presented which allows for greater orientation selectivity, while maintaining the orthogonality and data volume of the conventional wavelet transform. Many designs for vector quantizers have been published recently and another is added to the gamut by this work. The tree structured vector quantizer presented here is on-line and self structuring, requiring no distinct training phase. Combining these into a still image data compression system produces results which are among the best that have been published to date. An extension of the two dimensional wavelet transform to encompass the time dimension is straightforward and this work attempts to extrapolate some of its properties into three dimensions. The vector quantizer is then applied to three dimensional image data to produce a video coding system which, while not optimal, produces very encouraging results

An overview Survey on Various Video compressions and its importance

With the rise of digital computing and visual data processing, the need for storage and transmission of video data became prevalent. Storage and transmission of uncompressed raw visual data is not a good practice, because it requires a large storage space and great bandwidth. Video compression algorithms can compress this raw visual data or video into smaller files with a little sacrifice on the quality. This paper an overview and comparison of standard efforts on video compression algorithm of: MPEG-1, MPEG-2, MPEG-4, MPEG-

Fractal methods in image analysis and coding

In this thesis we present an overview of image processing techniques which use fractal methods in some way. We show how these fields relate to each other, and examine various aspects of fractal methods in each area. The three principal fields of image processing and analysis th a t we examine are texture classification, image segmentation and image coding. In the area of texture classification, we examine fractal dimension estimators, comparing these methods to other methods in use, and to each other. We attempt to explain why differences arise between various estimators of the same quantity. We also examine texture generation methods which use fractal dimension to generate textures of varying complexity. We examine how fractal dimension can contribute to image segmentation methods. We also present an in-depth analysis of a novel segmentation scheme based on fractal coding. Finally, we present an overview of fractal and wavelet image coding, and the links between the two. We examine a possible scheme involving both fractal and wavelet methods

Fractal image compression and the self-affinity assumption : a stochastic signal modelling perspective

Bibliography: p. 208-225.Fractal image compression is a comparatively new technique which has gained considerable attention in the popular technical press, and more recently in the research literature. The most significant advantages claimed are high reconstruction quality at low coding rates, rapid decoding, and "resolution independence" in the sense that an encoded image may be decoded at a higher resolution than the original. While many of the claims published in the popular technical press are clearly extravagant, it appears from the rapidly growing body of published research that fractal image compression is capable of performance comparable with that of other techniques enjoying the benefit of a considerably more robust theoretical foundation. . So called because of the similarities between the form of image representation and a mechanism widely used in generating deterministic fractal images, fractal compression represents an image by the parameters of a set of affine transforms on image blocks under which the image is approximately invariant. Although the conditions imposed on these transforms may be shown to be sufficient to guarantee that an approximation of the original image can be reconstructed, there is no obvious theoretical reason to expect this to represent an efficient representation for image coding purposes. The usual analogy with vector quantisation, in which each image is considered to be represented in terms of code vectors extracted from the image itself is instructive, but transforms the fundamental problem into one of understanding why this construction results in an efficient codebook. The signal property required for such a codebook to be effective, termed "self-affinity", is poorly understood. A stochastic signal model based examination of this property is the primary contribution of this dissertation. The most significant findings (subject to some important restrictions} are that "self-affinity" is not a natural consequence of common statistical assumptions but requires particular conditions which are inadequately characterised by second order statistics, and that "natural" images are only marginally "self-affine", to the extent that fractal image compression is effective, but not more so than comparable standard vector quantisation techniques

Multiple Description Wavelet-Based Image Coding Using Iterated Function System

Recent literature highlights the multiple description coding (MDC) as a promising method to solve the problem of resilient image coding over error-prone networks, where packet losses occur. In this paper, we introduce a novel multiple description wavelet-based image coding scheme using fractal. This scheme exploits the fractal’s ability, which is to describe the different resolution scale similarity (redundancy) among wavelet coefficient blocks. When one description is lost, the lost information can be reconstructed by the proposed iterated function system (IFS) recovering scheme with the similarity and some introduced information. Compared with the referenced methods, the experimental results suggest that the proposed scheme can achieve better performance. Furthermore, it is substantiated to be more robust for images transmission and better subjective quality in reconstructed images even with high packet loss ratios