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Open AccessArticle

GPU Acceleration of the Most Apparent Distortion Image Quality Assessment Algorithm

1
School of Computing Informatics and Decision Systems Engineering, Arizona State University, Tempe, AZ 85281, USA
2
The Polytechnic School, Arizona State University, Mesa, AZ 85212, USA
3
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
4
Department of Electrical and Electronic Engineering, Shizuoka University, Hamamatsu, Shizuoka 432-8561, Japan
*
Author to whom correspondence should be addressed.
J. Imaging 2018, 4(10), 111; https://doi.org/10.3390/jimaging4100111
Received: 1 August 2018 / Revised: 10 September 2018 / Accepted: 19 September 2018 / Published: 25 September 2018
(This article belongs to the Special Issue Image Quality)
The primary function of multimedia systems is to seamlessly transform and display content to users while maintaining the perception of acceptable quality. For images and videos, perceptual quality assessment algorithms play an important role in determining what is acceptable quality and what is unacceptable from a human visual perspective. As modern image quality assessment (IQA) algorithms gain widespread adoption, it is important to achieve a balance between their computational efficiency and their quality prediction accuracy. One way to improve computational performance to meet real-time constraints is to use simplistic models of visual perception, but such an approach has a serious drawback in terms of poor-quality predictions and limited robustness to changing distortions and viewing conditions. In this paper, we investigate the advantages and potential bottlenecks of implementing a best-in-class IQA algorithm, Most Apparent Distortion, on graphics processing units (GPUs). Our results suggest that an understanding of the GPU and CPU architectures, combined with detailed knowledge of the IQA algorithm, can lead to non-trivial speedups without compromising prediction accuracy. A single-GPU and a multi-GPU implementation showed a 24× and a 33× speedup, respectively, over the baseline CPU implementation. A bottleneck analysis revealed the kernels with the highest runtimes, and a microarchitectural analysis illustrated the underlying reasons for the high runtimes of these kernels. Programs written with optimizations such as blocking that map well to CPU memory hierarchies do not map well to the GPU’s memory hierarchy. While compute unified device architecture (CUDA) is convenient to use and is powerful in facilitating general purpose GPU (GPGPU) programming, knowledge of how a program interacts with the underlying hardware is essential for understanding performance bottlenecks and resolving them. View Full-Text
Keywords: image quality assessment; performance analysis; GPU computing; Most Apparent Distortion image quality assessment; performance analysis; GPU computing; Most Apparent Distortion
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MDPI and ACS Style

Holloway, J.; Kannan, V.; Zhang, Y.; Chandler, D.M.; Sohoni, S. GPU Acceleration of the Most Apparent Distortion Image Quality Assessment Algorithm. J. Imaging 2018, 4, 111. https://doi.org/10.3390/jimaging4100111

AMA Style

Holloway J, Kannan V, Zhang Y, Chandler DM, Sohoni S. GPU Acceleration of the Most Apparent Distortion Image Quality Assessment Algorithm. Journal of Imaging. 2018; 4(10):111. https://doi.org/10.3390/jimaging4100111

Chicago/Turabian Style

Holloway, Joshua; Kannan, Vignesh; Zhang, Yi; Chandler, Damon M.; Sohoni, Sohum. 2018. "GPU Acceleration of the Most Apparent Distortion Image Quality Assessment Algorithm" J. Imaging 4, no. 10: 111. https://doi.org/10.3390/jimaging4100111

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