A Mixture of Experts Approach to Multi-strategy Image Quality Assessment

Peng, Peng; Li, Ze-Nian

doi:10.1007/978-3-642-31295-3_15

Cited by 8 publications

(11 citation statements)

References 11 publications

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“…In order to show dataset independent results, each introduced multimeasure was evaluated on all datasets. In the literature, some authors used different numbers of images from benchmarks for this purpose, e.g., 30% [13], [15], a one dataset [29], [24], [30], [18], [35], or even several datasets jointly, as in [20]. The following four IQA benchmarks were used: TID2013 [2], TID2008 [43], CSIQ [23], and LIVE [8], they are characterised in Table 1.…”

Section: Optimisation Resultsmentioning

confidence: 99%

“…In the table, the best three results for a given benchmark are written in boldface, results not reported are denoted by "-". Some measures are not benchmark independent, i.e., their authors reported evaluation results on the benchmark that took part in the development of the measure without providing results for other benchmarks, or prepared a one IQA measure for each benchmark, also without cross-benchmark tests, e.g., [24], [26], [27], [29], [30], [31], [35], [36]. Results for approaches that are not dataset independent were excluded from comparison, they are written in italics in the table.…”

Section: Discussionmentioning

confidence: 99%

“…In [29], a preliminary approach was presented with a regularized multi-dimensional polynomial estimator for combining seven IQA measures. A conditional Bayesian mixture of experts model with a support vector machines classifier was used in [30] for combining SSIM, VSNR, and VIF using k-nearest-neighbour regression. A support vector regression approach was shown in [31].…”

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: Discussionmentioning

confidence: 99%

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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“…Here, the best three results for a given benchmark are written in boldface, results not reported are denoted by "-". Furthermore, "-" denotes overall results for IQA measures that were not evaluated on these three benchmarks or are not independent, i.e., authors developed a separate measure for each benchmark without providing cross-benchmark results ( [13,18,20,22,25,48]). …”

Section: Rmse(smentioning

confidence: 99%

“…In [17], SNR, SSIM, VIF, and VSNR were combined using canonical correlation analysis, and a regularised regression was used to combine up to seven IQA models in [13]. In [25], a support vector machines classifier used for predicting of the distortion followed by a fusion of SSIM, VSNR, and VIF using knearest-neighbour regression, was proposed. An adaptive combination of IQA approaches with an edge-quality based on preservation of edge direction was introduced in [26].…”

Section: Introductionmentioning

confidence: 99%

Image quality assessment with lasso regression and pairwise score differences

Oszust

2016

Multimed Tools Appl

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The reception of multimedia applications often depends on the quality of processed and displayed visual content. This is the main reason for the development of automatic image quality assessment (IQA) techniques which try to mimic properties of human visual system and produce objective scores for evaluated images. Most of them require a training step in which subjective scores, obtained in tests with human subjects, are used for parameters tuning. In this paper, it is shown that pairwise score differences (PSD) can be successfully used for training a full-reference hybrid IQA measure based on the least absolute shrinkage and selection operator (lasso) regression. The results of extensive experimental evaluation on four largest IQA benchmarks show that the proposed IQA technique is statistically better than its version trained using raw scores, and both approaches are statistically better than state-of-the-art full-reference IQA measures. They are also better than other hybrid approaches. In the paper, the evaluation protocol is extended with tests using PSD.

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Decision Fusion for Image Quality Assessment using an Optimization Approach

Oszust

2016

IEEE Signal Process. Lett.

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A Mixture of Experts Approach to Multi-strategy Image Quality Assessment

Cited by 8 publications

References 11 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

Image quality assessment with lasso regression and pairwise score differences

Decision Fusion for Image Quality Assessment using an Optimization Approach

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