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Microarchitectural analysis of image quality assessment algorithms

[+] Author Affiliations
Thien D. Phan

Oklahoma State University, Laboratory of Computational Perception and Image Quality, School of Electrical and Computer Engineering, Stillwater, Oklahoma 74078

Siddharth K. Shah

Oklahoma State University, Computer Architecture Education Simulation and Research, School of Electrical and Computer Engineering, Stillwater, Oklahoma 74078

Damon M. Chandler

Oklahoma State University, Laboratory of Computational Perception and Image Quality, School of Electrical and Computer Engineering, Stillwater, Oklahoma 74078

Sohum Sohoni

Arizona State University, Department of Engineering and Computing Systems, Mesa, Arizona 85212

J. Electron. Imaging. 23(1), 013030 (Feb 26, 2014). doi:10.1117/1.JEI.23.1.013030
History: Received October 4, 2013; Revised January 7, 2014; Accepted January 28, 2014
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Abstract.  Algorithms for image quality assessment (IQA) aim to predict the qualities of images in a manner that agrees with subjective quality ratings. Over the last several decades, the major impetus in IQA research has focused on improving predictive performance; very few studies have focused on analyzing and improving the runtime performance of IQA algorithms. This paper is the first to examine IQA algorithms from the perspective of their interaction with the underlying hardware and microarchitectural resources, and to perform a systematic performance analysis using state-of-the-art tools and techniques from other computing disciplines. We implemented four popular full-reference IQA algorithms (most apparent distortion, multiscale structural similarity, visual information fidelity, and visual signal-to-noise ratio) and two no-reference algorithms (blind image integrity notator using DCT statistics and blind/referenceless image spatial quality evaluator) in C++ based on the code provided by their respective authors. We then conducted a hotspot analysis to identify sections of code that were performance bottlenecks and performed microarchitectural analysis to identify the underlying causes for these bottlenecks. Despite the fact that all six algorithms share common algorithmic operations (e.g., filterbanks and statistical computations), our results revealed that different IQA algorithms overwhelm different microarchitectural resources and give rise to different types of bottlenecks. Based on these results, we propose microarchitectural-conscious coding techniques and custom hardware recommendations for performance improvement.

© 2014 SPIE and IS&T

Citation

Thien D. Phan ; Siddharth K. Shah ; Damon M. Chandler and Sohum Sohoni
"Microarchitectural analysis of image quality assessment algorithms", J. Electron. Imaging. 23(1), 013030 (Feb 26, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.1.013030


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