SR-SIM: A fast and high performance IQA index based on spectral residual

Zhang, Lin; Li, Hongyu

doi:10.1109/icip.2012.6467149

Cited by 215 publications

(146 citation statements)

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“…In experiments, the following 16 IQA measures with publicly available objective scores, or source-code, for used benchmarks took part in the optimisation: VSI [15], FSIM [13], FSIMc [13], GSM [19], IFC [12], IW-SSIM [10], MAD [23], MSSIM [9], NQM [6], PSNR [38], RFSIM [16], SR-SIM [14], SSIM [8], VIF [11], IFS [41], and SFF [42]. It is worth noting that MAD is a multimeasure, but it was used in the optimisation due to its popularity and availability of the source-code.…”

Section: Optimisation Resultsmentioning

confidence: 99%

“…One of the simplest techniques in this category is peak signalto-noise ratio (PSNR); noise quality measure (NQM) [6], in turn, uses a linear frequency distortion and an additive noise injection. Other measures which have been introduced in the last decade use: luminance and contrast distortions [7], structural information [8], [9], [10], statistical properties [10], [11], [12], phase congruency and image gradient magnitude [13], visual saliency maps [14], [15], Riesz-transform features [16], speeded-up robust features [17], local binary patterns [18], structure and contrast changes [19], inter-patch and intra-patch similarities [20], or fuzzy gradient similarity deviation [21].…”

Section: Introductionmentioning

confidence: 99%

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A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Oszust

2016

Measurement Science Review

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The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is consistent with human visual perception. In this paper, an approach to full-reference image quality assessment (IQA) is proposed, in which several IQA measures, representing different approaches to modelling human visual perception, are efficiently combined in order to produce objective quality evaluation of examined images, which is highly correlated with evaluation provided by human subjects. In the paper, an optimisation problem of selection of several IQA measures for creating a regression-based IQA hybrid measure, or a multimeasure, is defined and solved using a genetic algorithm. Experimental evaluation on four largest IQA benchmarks reveals that the multimeasures obtained using the proposed approach outperform state-of-the-art full-reference IQA techniques, including other recently developed fusion approaches.

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Section: Optimisation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Oszust

2016

Measurement Science Review

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“…Consequently, in this paper, we attempt to extent TMQI by proposing a novel VS-based pooling strategy. Specifically, in our method, we use VS as a weight map, which is inspired by its successful use on IQA [4,5]. The experimental results demonstrate that the VS-based pooling strategy can achieve significantly better performance than the simple "mean" that used in TMQI.…”

Section: Introductionmentioning

confidence: 96%

“…However, no one tries to use VS-based pooling strategy in the objective quality assessment of tonemapped images. In this paper, we tried 5 kinds of VS models, but in this section we only give a brief introduction about two of them which we found have outstanding performance when used in IQA [5] or saliency detection [7], namely SR and SDSP.…”

Section: Two Visual Saliency Modelsmentioning

confidence: 99%

Integrating Visual Saliency Information into Objective Quality Assessment of Tone-Mapped Images

Liu

Zhang

et al. 2014

Intelligent Computing Theory

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Abstract. Tone-mapped images are the low dynamic range (LDR) images converted from high dynamic range (HDR) images. Recently, the objective quality assessment of tone-mapped images is becoming a challenging problem. However, there is no mature algorithm to deal with this issue until the tone-mapped image quality index (TMQI) was proposed recently, which is tone-mapped image quality index (TMQI). Unfortunately, the pooling method of the structural fidelity map in TMQI is the simple "mean", which makes the result unsatisfying. On the other hand, recent studies have found that different locations of an image may have different contributions to the quality perception of the human visual system. The significance of a local image region can be well characterized by a visual saliency (VS) model. Inspired by this insight, in this paper, we propose a VS-based pooling strategy for the objective quality assessment of tone-mapped images. The experimental results clearly demonstrate the efficacy of our proposed method.Keywords: Tone-mapped images, high dynamic range images, objective quality assessment, visual saliency. IntroductionRecently, researchers have shown a growing interest in high dynamic range (HDR) images. Compared to the low dynamic range (LDR) images, the range of intensity levels of HDR images is largely wider, thus enjoying advantages. Specifically, the range of intensity level of HDR images could be on the order of 10000 to 1, which allows for accurate representations of luminance variations in real scenes, ranging from direct sunlight to faint starlight [1], while the LDR images only have 256 intensity levels. However, General displays are designed for displaying LDR images, so we must use tone-mapping operators (TMOs) to create the corresponding LDR images for the visualization of HDR images. As the reduction in dynamic range, the LDR images converted from HDR images cannot preserve all the information. With an increasing number of TMOs being developed, a question appears, that is which TMO has a better performance? This question drives us to develop an index to compare them, and then utilize the index for optimizing parameters in TMOs.

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“…Estimating the distribution of such points, or computing a visual saliency map [11,12] has been proven to be of great help in improving the performance of FR-IQA algorithms. As Zhang et al has proposed in [13], an image's visual saliency map is closely related to its perceived quality and it can be used as a weighting function indicating the importance of a local region to the human visual system. There are already many different kinds of algorithms proposed for visual saliency computation in the literature.…”

Section: Sampling Strategymentioning

confidence: 99%

Learning a blind image quality index based on visual saliency guided sampling and Gabor filtering

Zhang

2013

2013 IEEE International Conference on Image Processing

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The goal of no-reference image quality assessment (NR-IQA) is to estimate the quality of an image consistent with the human perception of the image automatically without any prior of the reference image. In this paper, we present a simple yet efficient and effective approach to learn a blind Image Quality index based on Visual saliency guided sampling and Gabor filtering, namely IQVG. Given an image, we at first randomly sample a sufficient number of image patches guided by the image's visual saliency map and convolve each patch with Gabor filters to get a bag of features. Then, the image is represented by using a histogram to encode the bag of features. Support vector regression (SVR) is used to learn the mapping from feature space to image quality. Extensive experiments conducted on the LIVE IQA database demonstrate the overall superiority of our IQVG over the other state-of-the-art NR-IQA algorithms evaluated. The Matlab source code of IQVG and the evaluation results are available online at

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SR-SIM: A fast and high performance IQA index based on spectral residual

Cited by 215 publications

References 8 publications

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

Integrating Visual Saliency Information into Objective Quality Assessment of Tone-Mapped Images

Learning a blind image quality index based on visual saliency guided sampling and Gabor filtering

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