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Real-time dynamic tone-mapping operator on GPU

Mohamed Akil, Thierry Grandpierre, Laurent Perroton
Univesite Paris Est, ESIEE, Marne-la-valle, France
Journal of Real-Time Image Processing (21 February 2011), pp. 1-8

@article{akilreal,

   title={Real-time dynamic tone-mapping operator on GPU},

   author={Akil, M. and Grandpierre, T. and Perroton, L.},

   journal={Journal of Real-Time Image Processing},

   pages={1–8},

   issn={1861-8200},

   publisher={Springer}

}

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This article presents the parallel implementation on a GPU of a real-time dynamic tone-mapping operator. The operator we describe in this article is generic and may be used by any application. However, the goal of our work is to integrate this operator into the graphic rendering process of a car driving simulator; thus, we studied its real-time implementation. The tone-mapping operator outputs a low dynamic range (LDR) image keeping as much as possible the contrast and luminance of the original input high dynamic range (HDR) image. It performs the mapping between the luminances of the original scene to the output device’s display values. We address the problem of mapping HDR images to standard displays. In this case, the tone mapping compresses the luminances ratio. Several tone-mapping operators can be found in the literature as well as some parallelizations. However, they use either static or adaptations of static operators. We have adapted the dynamic operator of Irawan and parallelized it on GPU. Algorithmic optimizations have been performed, and we have explored the different strategies of repartition of the computation among the CPU and the GPU. We have chosen to implement on the GPU the changes between the color spaces and the interpolation of the histogram which are the most time-consuming steps on the CPU (1-2 s per image 1,002 x 666). All of these optimizations lead to an increase of the processing rate and the number of HDR-quality images displayed to LDR per second. This operator has been implemented on a RISC processor Pentium 4 at 3.6 GHz and a GPU Nvidia 8800 GTX (728MB, 518GFLOPS). The execution speed has been multiplied by a factor of 15 compared to the naive implementation of the algorithm. The display rate reaches 30 images per second, which fulfills our goal for real-time video rate of 25 images per second.
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