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

Fractal Image Compression on MIMD Architectures II: Classification Based Speed-up Methods

Since fractal image compression is computationally very expensive, speed-up techniques are required in addition to parallel processing in order to compress large images in reasonable time. In this paper we discuss parallel fractal image compression algorithms suited for MIMD architectures which employ block classification as speed-up method

Distributed video through telecommunication networks using fractal image compression techniques

The research presented in this thesis investigates the use of fractal compression techniques for a real time video distribution system. The motivation for this work was that the method has some useful properties which satisfy many requirements for video compression. In addition, as a novel technique, the fractal compression method has a great potential. In this thesis, we initially develop an understanding of the state of the art in image and video compression and describe the mathematical concepts and basic terminology of the fractal compression algorithm. Several schemes which aim to the improve of the algorithm, for still images are then examined. Amongst these, two novel contributions are described. The first is the partitioning of the image into sections which resulted insignificant reduction of the compression time. In the second, the use of the median metric as alternative to the RMS was considered but was not finally adopted, since the RMS proved to be a more efficient measure. The extension of the fractal compression algorithm from still images to image sequences is then examined and three different schemes to reduce the temporal redundancy of the video compression algorithm are described. The reduction in the execution time of the compression algorithm that can be obtained by the techniques described is significant although real time execution has not yet been achieved. Finally, the basic concepts of distributed programming and networks, as basic elements of a video distribution system, are presented and the hardware and software components of a fractal video distribution system are described. The implementation of the fractal compression algorithm on a TMS320C40 is also considered for speed benefits and it is found that a relatively large number of processors are needed for real time execution

A Review on Block Matching Motion Estimation and Automata Theory based Approaches for Fractal Coding

Fractal compression is the lossy compression technique in the field of gray/color image and video compression. It gives high compression ratio, better image quality with fast decoding time but improvement in encoding time is a challenge. This review paper/article presents the analysis of most significant existing approaches in the field of fractal based gray/color images and video compression, different block matching motion estimation approaches for finding out the motion vectors in a frame based on inter-frame coding and intra-frame coding i.e. individual frame coding and automata theory based coding approaches to represent an image/sequence of images. Though different review papers exist related to fractal coding, this paper is different in many sense. One can develop the new shape pattern for motion estimation and modify the existing block matching motion estimation with automata coding to explore the fractal compression technique with specific focus on reducing the encoding time and achieving better image/video reconstruction quality. This paper is useful for the beginners in the domain of video compression

A VHDL design for hardware assistance of fractal image compression

Fractal image compression schemes have several unusual and useful attributes, including resolution independence, high compression ratios, good image quality, and rapid decompression. Despite this, one major difficulty has prevented their widespread adoption: the extremely high computational complexity of compression. Fractal image compression algorithms represent an image as a series of contractive transformations, each of which maps a large domain block to a smaller range block. Given only this set of transformations, it is possible to reconstruct an approximation of the original image by iteratively applying the transformations to an arbitrary image. Compression consists of partitioning the image into range blocks and finding a suitable transformation of a domain block to represent each one. This search for transformations must generally be done using a brute force approach, comparing successive domain blocks until a suitable match is found. Some algorithmic improvements have been found, but none are adequate to reduce the required compression time to something reasonable for many uses. This thesis presents a new ASIC design which performs a large number of the required comparisons in parallel, yielding a substantial speedup over a program on a general-purpose computer system. This ASIC is designed in VHDL, which may be synthesized to many different target architectures. The design has considerable flexibility which makes it applicable to different images and applications. The design is based around a pipeline of units that each compare one range block with a series of domain blocks which are fed through the pipeline. Comparisons are made to minimize the mean square error (MSE) of a transform given a linear mapping of the intensity values. This is, by far, the most common minimization strategy used in the literature. The speedup provided by this design is estimated to be about 1,000 times for 256 x 256 images divided into 8x8 blocks over a sequential processor given similar implementation technologies

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

A reduced domain pool based on DCT for a fast fractal image encoding

Fractal image compression is time consuming due to the search of the matching between range and domain blocks. In order to improve this compression method, we propose firstly, in this paper, a fast method for reducing the computational complexity of fractal encoding by reducing the size of the domain pool. This reduction is based on the lowest horizontal and vertical DCT coefficients of domain blocks. The experimental results on the test images show that the proposed method reduce the time computation and reach a high speedup factor without decreasing the image quality. Secondly, we combine our method to the AP2D approach which uses two domain pools in two steps of encoding. A more reduction of encoding time is obtained without decreasing the image quality

A Fast Fractal Image Compression Algorithm Combined with Graphic Processor Unit

Directed against the characteristics of computational intensity of fractal image compression encoding, a serial-parallel transfer mechanism is built for encoding procedures. By utilizing the properties of single instruction and multithreading execution of compute unified device architecture (CUDA), the parallel computational model of fractal encoding is built on the graphic processor unit(GPU) in order to parallelize the considerably time-consuming serial execution process of searching for the block of best match. The experimental result indicates, the algorithm in this paper shortens the encoding time to the millisecond scale and significantly boosts the execution efficiency of fractal image encoding algorithm while keeping the decoded image in good quality