A GPGPU-based software implementation of the PBDI deinterlacing algorithm

Kowarzyk, G.; Belanger, N.; Savaria, Yvon

doi:10.1109/icecs.2011.6122390

Cited by 2 publications

(2 citation statements)

References 8 publications

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“…Kowarzyk et al [36] presented a parallel implementation of a deinterlacing method. It is GPGPU-based software implementation of the PBDI deinterlacing algorithm.…”

Section: Introductionmentioning

confidence: 99%

GPU-Parallel Implementation of the Edge-Directed Adaptive Intra-Field Deinterlacing Method

Song

Jeon

2014

J. Display Technol.

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This paper proposes an efficient GPU-based massively parallel implementation of the edge-directed adaptive intra-field deinterlacing method which interpolates the missing pixels based on the deinterlaced covariance estimated from the interlaced covariance according to the geometric duality between the interlaced and the deinterlaced covariance. Although the edge-directed adaptive intra-field deinterlacing method can obtain better visual quality than conventional intra-field deinterlacing methods, the time-consuming computation is usually the bottleneck of this deinterlacing method. In order to tackle the problem, Graphics Processing Units (GPUs), as opposed to traditional CPU architectures, are better candidates to speed up the computation process. The proposed method interpolates more than one missing pixel at a time in order to gain a significant speedup compared to the case of interpolating just one missing pixel at a time. Experimental results show that we obtained a speedup of 94.6 when the I/O transfer time was taken into account, compared to the original single-threaded C CPU code with the -O2 compiling optimization.

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“…Kowarzyk et al [36] presented a parallel implementation of a deinterlacing method. It is GPGPU-based software implementation of the PBDI deinterlacing algorithm.…”

Section: Introductionmentioning

confidence: 99%

GPU-Parallel Implementation of the Edge-Directed Adaptive Intra-Field Deinterlacing Method

Song

Jeon

2014

J. Display Technol.

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“…Benefiting from increasing of the computing capability of GPUs, more and more scientists and engineers use them as cost-effective high-performance computing platforms. Inspired by the successful application of the GPU in various domains [9][10][11][12][13], especially in image processing field, we will develop, over the course of this paper, a massively parallel version of the autoregressive modeling image interpolation method to run on a NVIDIA GeForce GTX 480 GPU. In the autoregressive modeling image interpolation method, the missing pixels in HR image are estimated independently, although the contextual of each pixel is overlapping.…”

Section: Introductionmentioning

confidence: 99%

GPU-parallel implementation of the autoregressive model interpolation for high-resolution remote sensing images

Song

Jeon

2013

SPIE Proceedings

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The autoregressive modeling image interpolation scheme is noticeably closer to ideal interpolation aiming at obtaining a high-resolution (HR) image from its low-resolution (LR) version than conventional methods. The basic idea is to first estimate the covariance of HR image from the covariance of the LR image and then adjust the covariance coefficients of HR image according to a feedback mechanism that takes into account the mutual influence between the estimated missing pixels in a local window. In spite of its impressive performance, the time-consuming computation is usually the bottleneck of the method when it is applied in time-critical scenario. Graphics Processing Units (GPUs) are attractive candidates to expedite the computation process. In this paper, an efficient GPU-based massively parallel version of the autoregressive modeling image interpolation scheme was proposed. Because all pixels which need to be interpolated have no dependence, each estimated pixel is assigned to independent thread in our parallel interpolation scheme. Experimental results show that we reached a speedup of 21.2x when I/O transfer time was taken into account, with respect to the original single-threaded C CPU code with the -O2 compiling optimization.

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A GPGPU-based software implementation of the PBDI deinterlacing algorithm

Cited by 2 publications

References 8 publications

GPU-Parallel Implementation of the Edge-Directed Adaptive Intra-Field Deinterlacing Method

GPU-Parallel Implementation of the Edge-Directed Adaptive Intra-Field Deinterlacing Method

GPU-parallel implementation of the autoregressive model interpolation for high-resolution remote sensing images

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