CUDA implementation of fractal image compression

Abir Al Sideiri*, Nasser Alzeidi, Mayyada Al Hammoshi, Munesh Singh Chauhan, Ghaliya AlFarsi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Fractal coding is a lossy image compression technique, which encodes the image in a way that would require less storage space using the self-similar nature of the image. The main drawback of fractal compression is the high encoding time. This is due to the hard task of finding all fractals during the partition step and the search for the best match of fractals. Lately, GPUs (Graphical Processing Unit) have been exploited to implement fractal image compression algorithms due to their high computational power. The prime aim of this paper is to design and implement a parallel version of the Fisher classification scheme using CUDA to exploit the computational power available in the GPUs. Fisher classification scheme is used to reduce the encoding time of fractal images by limiting the search for the best match of fractals. Encoding time, compression ratio and peak signal-to-noise ratio was used as metrics to assess the correctness and the performance of the developed algorithm. Eight images with different sizes (512 × 512, 1024 × 1024 and 2048 × 2048) have been used for the experiments. The conducted experiments showed that a speedup of 6.4 × was achieved in some images using NVIDIA GeForce GT 660 M GPU.

Original languageEnglish
Pages (from-to)1375-1387
Number of pages13
JournalJournal of Real-Time Image Processing
Volume17
Issue number5
DOIs
Publication statusPublished - Oct 1 2020

Keywords

  • CUDA
  • Fractal image compression
  • GPU
  • Parallel processing
  • Quad-tree partitioning

ASJC Scopus subject areas

  • Information Systems

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