DCT Implementation on GPU

There has been a great progress in the field of graphics processors. Since, there is no rise in the speed of the normal CPU processors; Designers are coming up with multi-core, parallel processors. Because of their popularity in parallel processing, GPUs are becoming more and more attractive for many applications. With the increasing demand in utilizing GPUs, there is a great need to develop operating systems that handle the GPU to full capacity. GPUs offer a very efficient environment for many image processing applications. This thesis explores the processing power of GPUs for digital image compression using Discrete cosine transform

Development and Comparison of Image Encoders Based on Different Compression Techniques

In this project we present some of the most relevant image compression methods of the digital era. From lossless compression techniques like Laplacian Pyramid, to the current and frequently used lossy JPEG compression techniques, going through techniques that have been very influential in the past, as Fractal compression. The project is organized by chapters describing briefly the algorithms and presenting the results obtained when applied to three different test images. Finally we perform a comparative analysis synthesizing the main results. In this analysis we see the large difference in compression ratios between lossy and lossless compression algorithms. We also compare our developed lossy algorithms, observing that the first Fractal algorithm gives poor PSNR results, while our second Fractal algorithm and the JPEG algorithm give quite better qualities of compression; the latter achieving results comparable to present day JPEG algorithm

Fractal image compression on spiral architecture

Image compression has many applications. For example, it is an important step for distributed and network based pattern recognition. For real time object recognition or reconstruction, image compression can greatly reduce the image size, and hence increase the processing speed and enhance performance. Fractal image compression is a relatively recent image compression method. Its basic idea is to represent images as a fixed point of a contractive Iterated Function System (IFS). Spiral Architecture (SA) is a novel image structure on which images are displayed as a collection of hexagonal pixels. The efficiency and accuracy of image processing on SA have been demonstrated in many recently published papers. We have shown the existence of contractive IFS's through the construction of a Complete Metric Space on SA. The selection of range and domain blocks for fractal image compression is highly related to the uniform image separation specific to SA. In this paper, we will review the current research work on fractal image compression based on SA. We will compare the results obtained on SA and the traditional square structure in terms of compression ratio and PSNR. © 2006 IEEE

Fractal image compression on spiral architecture

University of Technology, Sydney. Faculty of Information Technology.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. The hardcopy may be available for consultation at the UTS Library.NO FULL TEXT AVAILABLE. Access is restricted indefinitely. ----- In this thesis, the previous research work on Spiral Architecture will be reviewed and the existing representation of SA will be improved to produce better performance. Additionally, the Fractal Image Compression (FIC) algorithm will be adapted to the Spiral Architecture for the first time using the improved representation. Comparisons will be carried out between the FIC performances on SA and square structure. The application of progressive FIC decoding and an FIC improved algorithm based on a difference image will also be discussed. The main purpose of the research work in this thesis is to introduce a potentially new direction of research in Fractal Image Compression by adopting it onto a hexagonal image structure - Spiral Architecture, so that others may bring forth valuable better results. The methods to be presented in this thesis can potentially be improved in various ways. This thesis develops and extends research results obtained by the author while preparing for the PhD since 2004. Most of the results have been previously published in sixteen research papers in refereed conference proceedings and journals