CUDA implementation of fractal image compression
Encoding (symbols); Fractals; Graphics processing unit; Image coding; Program processors; Signal encoding; Signal to noise ratio; CUDA; Fractal image compression; Fractal image compression algorithm; Graphical processing unit (GPUs); Lossy image compression; Parallel processing; Peak signal to noise...
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2023
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my.uniten.dspace-252282023-05-29T16:07:27Z CUDA implementation of fractal image compression Al Sideiri A. Alzeidi N. Al Hammoshi M. Chauhan M.S. AlFarsi G. 57207830966 15922193400 57209828089 37020134500 57194571355 Encoding (symbols); Fractals; Graphics processing unit; Image coding; Program processors; Signal encoding; Signal to noise ratio; CUDA; Fractal image compression; Fractal image compression algorithm; Graphical processing unit (GPUs); Lossy image compression; Parallel processing; Peak signal to noise ratio; Quad-tree partitioning; Image compression 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. � 2019, Springer-Verlag GmbH Germany, part of Springer Nature. Final 2023-05-29T08:07:27Z 2023-05-29T08:07:27Z 2020 Article 10.1007/s11554-019-00894-7 2-s2.0-85068847155 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068847155&doi=10.1007%2fs11554-019-00894-7&partnerID=40&md5=54821f5a125dd209fb8515714f370201 https://irepository.uniten.edu.my/handle/123456789/25228 17 5 1375 1387 Springer Scopus |
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| description |
Encoding (symbols); Fractals; Graphics processing unit; Image coding; Program processors; Signal encoding; Signal to noise ratio; CUDA; Fractal image compression; Fractal image compression algorithm; Graphical processing unit (GPUs); Lossy image compression; Parallel processing; Peak signal to noise ratio; Quad-tree partitioning; Image compression |
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57207830966 |
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57207830966 Al Sideiri A. Alzeidi N. Al Hammoshi M. Chauhan M.S. AlFarsi G. |
| format |
Article |
| author |
Al Sideiri A. Alzeidi N. Al Hammoshi M. Chauhan M.S. AlFarsi G. |
| spellingShingle |
Al Sideiri A. Alzeidi N. Al Hammoshi M. Chauhan M.S. AlFarsi G. CUDA implementation of fractal image compression |
| author_sort |
Al Sideiri A. |
| title |
CUDA implementation of fractal image compression |
| title_short |
CUDA implementation of fractal image compression |
| title_full |
CUDA implementation of fractal image compression |
| title_fullStr |
CUDA implementation of fractal image compression |
| title_full_unstemmed |
CUDA implementation of fractal image compression |
| title_sort |
cuda implementation of fractal image compression |
| publisher |
Springer |
| publishDate |
2023 |
| _version_ |
1806424201541713920 |
| score |
13.214268 |