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Parallel fractal image compression using quadtree partition with task and dynamic parallelism

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Abstract

Fractal image compression is a lossy compression technique based on the iterative function system, which can be used to reduce the storage space and increase the speed of data transmission. The main disadvantage of fractal image compression is the high computational cost of the encoding step, compared with the popular image compression based on discrete cosine transform. The aim of this paper is the development of parallel implementations of fractal image compression using quadtree partition. We develop two parallel implementations: the first one uses task parallelism over a multi-core system and the second uses dynamic parallelism over a GPU architecture. We show performance comparisons of the parallel implementations using standard images to compare the capabilities of these parallel architectures. The proposed parallel implementations achieve speedups over the serial implementation of approximately \(15 \times\) using the multi-core CPU and \(25 \times\) using the GPU.

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Acknowledgements

The authors acknowledge the support from “Laboratorio de Supercómputo del Bajío” through grant number 300832 from CONACyT. The research of the second author has been partially supported by SEP-CONACyT Grant A1-S-53349.

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  1. CONACYT—Centro de Investigación en Matemáticas A.C., CIMAT Unidad Mérida, PCTY, Sierra Papacal, 97302, Mérida, Yucatán, Mexico

    Francisco J. Hernandez-Lopez & Omar Muñiz-Pérez

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  1. Francisco J. Hernandez-Lopez
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  2. Omar Muñiz-Pérez
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Correspondence to Francisco J. Hernandez-Lopez.

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Hernandez-Lopez, F.J., Muñiz-Pérez, O. Parallel fractal image compression using quadtree partition with task and dynamic parallelism. J Real-Time Image Proc 19, 391–402 (2022). https://doi.org/10.1007/s11554-021-01193-w

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