Publication:
CUDA implementation of fractal image compression

Simple item page
dc.citedby7
dc.contributor.authorAl Sideiri A.en_US
dc.contributor.authorAlzeidi N.en_US
dc.contributor.authorAl Hammoshi M.en_US
dc.contributor.authorChauhan M.S.en_US
dc.contributor.authorAlFarsi G.en_US
dc.contributor.authorid57207830966en_US
dc.contributor.authorid15922193400en_US
dc.contributor.authorid57209828089en_US
dc.contributor.authorid37020134500en_US
dc.contributor.authorid57194571355en_US
dc.date.accessioned2023-05-29T08:07:27Z
dc.date.available2023-05-29T08:07:27Z
dc.date.issued2020
dc.descriptionEncoding (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 compressionen_US
dc.description.abstractFractal 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s11554-019-00894-7
dc.identifier.epage1387
dc.identifier.issue5
dc.identifier.scopus2-s2.0-85068847155
dc.identifier.spage1375
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85068847155&doi=10.1007%2fs11554-019-00894-7&partnerID=40&md5=54821f5a125dd209fb8515714f370201
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/25228
dc.identifier.volume17
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleJournal of Real-Time Image Processing
dc.titleCUDA implementation of fractal image compressionen_US
dc.typeArticleen_US
dspace.entity.typePublication
Files
Collections
SCOPUS