No-reference image quality assessment in complex-shearlet domain

Mahmoudpour, Saeed; Kim, Manbae

doi:10.1007/s11760-016-0957-7

Cited by 16 publications

(5 citation statements)

References 20 publications

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“…In this work we have investigated the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices in three different experiments, ranging from the use of features [31] 0.94 0.94 BLIINDS-II [39] 0.92 0.91 NIQE [32] 0.92 0.91 C-DIIVINE [51] 0.95 0.94 FRIQUEE [12,14] 0.95 0.93 ShearletIQM [29] 0.94 0.93 MGMSD [1] 0.97 0.97 Low Level Features [21] 0.95 0.94 Rectifier Neural Network [45] -0.96 Multi-task CNN [20] 0.95 0.95 Shallow CNN [19] 0.95 0.96 DLIQA [16] 0.93 0.93 HOSA [49] 0.95 0.95 CNN-Prewitt [27] 0.97 0.96 CNN-SVR [26] 0.97 0.96 DeepBIQ 0.98 0.97 [31] 0.93 0.91 BLIINDS-II [39] 0.93 0.91 Low Level Features [21] 0.94 0.94 Multi-task CNN [20] 0.93 0.94 HOSA [49] 0.95 0.93 DeepBIQ 0.97 0.96…”

Section: Discussionmentioning

confidence: 99%

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On the use of deep learning for blind image quality assessment

Bianco¹,

Celona²,

Napoletano³

et al. 2017

SIViP

285

190

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In this work we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained Convolutional Neural Networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple sub-regions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-ofthe-art methods compared, having a Linear Correlation Coefficient (LCC) with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008 and TID2013.

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Section: Discussionmentioning

confidence: 99%

“…[10,35,48,4,15,1], reduced-reference image quality assessment (RR-IQA) algorithms, and no-reference/blind image quality assessment (NR-IQA) algorithms e.g. [34,31,32,29,26,27]. FR-IQA algorithms perform a direct comparison between the image under test and a reference or original in a properly defined image space [7].…”

Section: Introductionmentioning

confidence: 99%

On the use of deep learning for blind image quality assessment

Bianco¹,

Celona²,

Napoletano³

et al. 2017

SIViP

285

190

View full text Add to dashboard Cite

show abstract

“…The proposed method is compared with several FR (PSNR, SSIM [1], FSIM [2] and HDR-VDP-2.2 [18]), RR (OSVP-RR [7]) and NR IQA (ShearletIQM [19], DIIVINE [3] and BLIINDS II [4]) metrics. Table I shows the performance of the proposed method on the MDID2015 database over 100 train-test iterations.…”

Section: Resultsmentioning

confidence: 99%

Reduced-reference image quality assessment based on internal generative mechanism utilizing shearlets and Rényi entropy analysis

Mahmoudpour

Schelkens

2017

2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX)

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During acquisition, processing, compression and transmission, images may be corrupted by multiple distortions such as blur, noise or compression artefacts. However, most of the existing image quality assessment (IQA) methods are designed for images degraded by a single distortion type. This paper proposes a reduced-reference (RR) IQA method for quality assessment of multiply distorted images. The method extracts a number of quality-characterizing features from the reference and the distorted images for quality prediction. Based on internal generative mechanism (IGM) theory, the images are decomposed first into their predicted and disorderly portions. Next, a number of quality-characterizing features are extracted from each portion and feature differences are computed between the reference and distorted images. Finally, support vector regression (SVR) is adopted to obtain a quality score. Experimental results on public multiply-distorted image databases, namely MDID2015 and MLIVE, show that the proposed method is well-correlated with subjective ratings and outperforms several IQA methods.

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“…The synthetic distortion based on NR-IQA can be grouped to: (1) natural scene statistical-based models (NSS), (2) manual feature extraction-based models and (3) deep learning-based models. There are a number of NSS-based NR-IQA methods such as BRISQUE [ 8 ], NIQE [ 9 ], BIQI [ 10 ], DIIVINE [ 11 ], BLIINDS [ 12 ], ShearletIQM [ 13 ] and SPNSS [ 14 ]. Moreover, CORINA [ 15 ] is NR-IQA-based model.…”

Section: Related Workmentioning

confidence: 99%

CSPP-IQA: a multi-scale spatial pyramid pooling-based approach for blind image quality assessment

Chen

Qin

et al. 2022

Neural Comput & Applic

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The traditional image quality assessment (IQA) methods are usually based on convolutional neural networks (CNNs). For these IQA methods using CNNs, limited by the feature size of the fully connected layer, the input image needs be tailored to a pre-defined size, which usually results in destroying the original structure and content of the input image and thus reduces the accuracy of the quality assessment. In this paper, a blind image quality assessment method (named CSPP-IQA), which is based on multi-scale spatial pyramid pooling, is proposed. CSPP-IQA allows inputting the original image when assessing the image quality without any image adjustment. Moreover, by facilitating the convolutional block attention module and image understanding module, CSPP-IQA achieved better accuracy, generalization and efficiency than traditional IQA methods. The result of experiments running on real-scene IQA datasets in this study verified the effectiveness and efficiency of CSPP-IQA.

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No-reference image quality assessment in complex-shearlet domain

Cited by 16 publications

References 20 publications

On the use of deep learning for blind image quality assessment

On the use of deep learning for blind image quality assessment

Reduced-reference image quality assessment based on internal generative mechanism utilizing shearlets and Rényi entropy analysis

CSPP-IQA: a multi-scale spatial pyramid pooling-based approach for blind image quality assessment

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