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

Performance Analysis of Distributed Implementations of a Fractal Image Compression Algorithm

Fractal image compression provides an innovative approach to lossy image encoding, with a potential for very high compression ratios. Because of prohibitive compression times, however, the procedure has proved feasible in only a limited range of commercial applications. In this paper we demonstrate that, due to the independent nature of fractal transform encoding of individual image segments, fractal image compression performs well in a coarsegrain distributed processing system. A sequential fractal compression algorithm is optimized and parallelized to execute across distributed workstations and an SP2 parallel processor using the Parallel Virtual Machine (PVM) software. The system utilizes both static and dynamic load allocation to obtain substantial compression time speedup over the original, sequential encoding implementation. Considerations such as workload granularity and compression time versus number of processors and RMS tolerance values are also presented. Keywords: Image com..