Performance-analysis-based acceleration of image quality assessment

Phan, Thien D.; Sohoni, Sohum; Chandler, Damon M.; Larson, Eric C.

doi:10.1109/ssiai.2012.6202458

Cited by 9 publications

(11 citation statements)

References 36 publications

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“…Thereafter, the sum of values within any block of the matrix can be rapidly computed via addition/subtraction of three values in the table. A similar technique can be used to compute higher-order moments such as the variance, skewness, and kurtosis (see, e.g., [362,363]).…”

Section: Acceleration Of Image Transforms and Local Statisticsmentioning

confidence: 99%

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

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375

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Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

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Section: Acceleration Of Image Transforms and Local Statisticsmentioning

confidence: 99%

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Chandler

2013

ISRN Signal Processing

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375

191

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“…Another contribution of this work is a unique implementation of BLIINDS-II's statistical operations designed specifically for use on the GPU. As a result, we are able to present to a version of BLIINDS-II that is capable of real-time performance, significantly improving upon the previously reported runtime after CPU optimizations in C++ [3]. A high speed of execution is critical in application of IQA to video delivery systems, where minimizing the lag is very important.…”

Section: Discussionmentioning

confidence: 81%

“…Metrics related to compute utilization, achieved memory bandwidth and latency were analyzed to achieve better runtime performance across various images of natural scenes with various distortions and varying levels of each. For all time comparisons, between the C++ implementation and the CUDA implementation of the algorithm, we have used the results reported in [3] for C++ and our own observations for CUDA. Table 1 provides technical specifications of our test system.…”

Section: Experimental Methodologymentioning

confidence: 99%

GPGPU based implementation of a high performing No Reference (NR) - IQA algorithm, BLIINDS-II

Yadav

Sohoni²,

Chandler

2017

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A relatively recent thrust in IQA research has focused on estimating the quality of a distorted image without access to the original (reference) image. Algorithms for this so-called noreference IQA (NR IQA) have made great strides over the last several years, with some NR algorithms rivaling full-reference algorithms in terms of prediction accuracy. However, there still remains a large gap in terms of runtime performance; NR algorithms remain significantly slower than FR algorithms, owing largely to their reliance on natural-scene statistics and other ensemble-based computations. To address this issue, this paper presents a GPGPU implementation, using NVidia's CUDA platform, of the popular Blind Image Integrity Notator using DCT Statistics (BLIINDS-II) algorithm [8], a state of the art NR-IQA algorithm. We copied the image over to the GPU and performed the DCT and the statistical modeling using the GPU. These operations, for each 5x5 pixel window, are executed in parallel. We evaluated the implementation by using NVidia Visual Profiler, and we compared the implementation to a previously optimized CPU C++ implementation. By employing suitable optimizations on code, we were able to reduce the runtime for each 512x512 image from approximately 270 ms down to approximately 9 ms, which includes the time for all data transfers across PCIe bus. We discuss our unique implementation of BLIINDS-II designed specifically for use on the GPU, the insights gained from the runtime analyses, and how the GPGPU techniques developed here can be adapted for use in other NR IQA algorithms.

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“…In [28], Phan et al proposed four techniques to accelerate the MAD IQA algorithm: (1) using integral images for the local statistical computation; (2) using procedural expansion and strength reduction; (3) using a GPGPU implementation of the log-Gabor decomposition; and (4) precomputation and caching of the log-Gabor filters. As reported in [22], the first two modifications yielded an approximate 17× speedup over the original MAD implementation, and the latter two yield an approximately 47× speedup.…”

Section: Acceleration Of Qa Algorithmsmentioning

confidence: 99%

“…In our previous related work [28], we compared the timings of naive vs. optimized C++ ports of MAD, and vs. using a Matlab-based GPU implementation of only the log-Gabor decomposition. Although significant speedups were obtained relative to the naive implementation, the use of the GPU contributed only a 1.6×-1.8× speedup over the CPU-based optimizations, which is reasonable considering only the log-Gabor decomposition was deployed on the GPU, and considering the fact that it was a Matlab-based GPU port.…”

Section: Introductionmentioning

confidence: 99%

GPU Acceleration of the Most Apparent Distortion Image Quality Assessment Algorithm

et al. 2018

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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.

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Performance-analysis-based acceleration of image quality assessment

Cited by 9 publications

References 36 publications

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

GPGPU based implementation of a high performing No Reference (NR) - IQA algorithm, BLIINDS-II

GPU Acceleration of the Most Apparent Distortion Image Quality Assessment Algorithm

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