Local Feature Descriptor and Derivative Filters for Blind Image Quality Assessment

Oszust, Mariusz

doi:10.1109/lsp.2019.2891416

Cited by 24 publications

(24 citation statements)

References 40 publications

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“…More recent approaches use local feature detectors to find regions in an image that are drawn by the HVS and then transform a set of descriptors assigned to them to a vector used for the quality prediction . However, their color‐based statistics or data‐driven filters may lead to inferior performance regarding MR images due to lack of color information, the inappropriateness of optimal filtering determined for natural images, or presence of unseen distortion types . To the best knowledge of the authors, local features have not been considered in the MR‐IQA literature.…”

Section: Methodsmentioning

confidence: 99%

“…The method was compared with the following 21 state‐of‐the‐art NR techniques with publicly available sourcecode: BRISQUE, NOREQI, BPRI, IL‐NIQE, HOSA, GWHGLBP, ORACLE, RATER, SCORER, QENI, GM‐LOG, SISBLIM, metricQ, SSEQ, S‐INDEX, NFERM, SEER, DEEPIQ, MEON, WaDIQaM‐NR, and SNRTOI . The NOREQI, BPRI, ORACLE, RATER, QENI, and SCORER are the most similar to NOMRIQA since they employ local features.…”

Section: Methodsmentioning

confidence: 99%

“…These measures represent a variety of approaches which have emerged in the IQA literature, including free‐energy principle, entropy, MSCN coefficients, deep learning methods, or distortion‐specific techniques . They also include previous authors’ works on IQA of natural images which employ gradient operators with real‐valued local features and image statistics, optimization‐based filters, derivative‐based filters with a quality‐aware feature descriptor, high‐pass filters and statistics of obtained binary descriptors, statistics of global descriptors considering differently defined local neighborhoods, or combined visual saliency models of image patches with self‐similarity of real‐valued local descriptors …”

Section: Introductionmentioning

confidence: 99%

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No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Oszust

Piórkowski

Obuchowicz

2020

Magnetic Resonance in Med

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Purpose: Subjective quality assessment of displayed magnetic resonance (MR) images plays a key role in diagnosis and the resultant treatment. Therefore, this study aims to introduce a new no-reference (NR) image quality assessment (IQA) method for the objective, automatic evaluation of MR images and compare its judgments with those of similar techniques. Methods: A novel NR-IQA method was developed. The method uses a sequence of scaled images filtered to enhance high-frequency components and preserve lowfrequency parts. Since the human visual system (HVS) is sensitive to local image variations and local features often mimic the attraction of the HVS to high-frequency image regions, they were detected in the filtered images and described. Then, the statistics of obtained descriptors were used to build a quality model via the Support Vector Regression method. Results: The method was compared with 21 state-of-the-art techniques for NR-IQA on a new dataset of 70 distorted MR images assessed by 31 experienced radiologists, using typical evaluation criteria for the comparison of NR measures. The introduced method significantly outperforms the compared approaches, in terms of the correlation with human judgments. Conclusions: It is demonstrated that the presented NR-IQA method for the assessment of MR images is superior to the state-of-the-art NR techniques. The method would be beneficial for a wide range of image processing applications, assessing their outputs and affecting the directions of their development. K E Y W O R D Shigh-boost filtering, image quality assessment, local features, magnetic resonance images, no-reference, subjective tests

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

Oszust

Piórkowski

Obuchowicz

2020

Magnetic Resonance in Med

Self Cite

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“…However, changes in natural statistical characteristics can be manifested in many ways; for instance, in [12], in the spatial domain, the regional mutual information of different subsets was calculated based on information lost due to distortion to predict the quality scores. Oszust [13] converted the RGB image into the YCbCr color space to extract the local features from the key points. Liu et al [14] extracted the statistical measurements from three representative aspects of structure, naturalness, and perception to unsupervised learning.…”

Section: B Nr-iqa Methodsmentioning

confidence: 99%

No-Reference Image Quality Assessment Based on Multi-Task Generative Adversarial Network

Cai

Sun

et al. 2019

IEEE Access

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Since human observers are the ultimate receivers of an image, most of the image quality assessment (IQA) methods are based on analysis of the properties and mechanism of the human visual system. However, due to the lack of undistorted images for a reference, the accuracy of the no-reference IQA (NR-IQA) cannot compete with that of the full-reference IQA (FR-IQA). To bridge the performance gap between the FR-IQA and NR-IQA methods, we propose a NR-IQA method based on multi-task generative adversarial network, which attempts to restore dependable hallucinated images to compensate for the missing corresponding reference images. Two tasks, hallucination images and the quality maps are outputted by the generator and are combined with the specific loss to improve the reliability of hallucination images. Besides, two discriminator networks are used to respectively distinguish the undistorted images and hallucination images pairs, quality maps and structural similarity index measurement maps pairs. Finally, the hallucination images and distorted images are input into the IQA network, and quality scores are evaluated based on the differences between them. The superiority of our proposed method is verified by several different experiments on the LIVE datasets, TID2008 datasets, and TID2013 datasets.INDEX TERMS Generative adversarial network, hallucination images, quality maps, IQA network.

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“…Generally, image quality assessment (IQA) metrics can be categorized into the full-reference (FR), reduced-reference (RR) and blind/no-reference methods. FR-IQA metrics need all the information of the original image, RR-IQA metrics only need part information of the original image, while blind IQA (BIQA) metrics predicate the image quality without any reference information [6]. For FR-IQA metrics, some classic metrics were proposed for ordinary 2D images, such as peak signal-to-noise-ratio (PSNR), structural similarity (SSIM) [7] and visual information fidelity (VIF) [8], and so on.…”

Section: Introductionmentioning

confidence: 99%

Segmented Spherical Projection-Based Blind Omnidirectional Image Quality Assessment

Zheng

Jiang

et al. 2020

IEEE Access

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In contrast with traditional images, omnidirectional image (OI) has a higher resolution and provides the user with an interactive wide field of view. OI with equirectangular projection (ERP) format, as the default for encoding and transmitting omnidirectional visual contents, is not suitable for quality assessment of OI because of serious geometric distortion in the bipolar regions, especially for blind image quality assessment. In this paper, a segmented spherical projection (SSP) based blind omnidirectional image quality assessment (SSP-BOIQA) method is proposed. The OI with ERP format is first converted into that with SSP format, so as to solve the problem of stretching distortion in the bipolar regions of ERP format, but retain the equatorial region of ERP format. On the one hand, considering that the bipolar regions of the SSP format are circular, a local/global perceptual features extraction scheme with fan-shaped window is proposed for estimating the distortion in the bipolar regions of OI. On the other hand, the perceptual features of the equatorial region are extracted with heat map as weighting factor to reflect users' visual behavior. Then, the features extracted from the OI's bipolar and equatorial regions are pooled to predict the quality of distorted OIs. The experiments on two databases, namely CVIQD2018 and MVAQD databases, demonstrate that the proposed SSP-BOIQA method outperforms the state-of-the-art blind quality assessment methods, and is more consistent with human visual perception. INDEX TERMS Omnidirectional image, blind quality assessment, segmented spherical projection, user's visual behavior, fan-shaped window. HAO JIANG received the M.S. degree from Ningbo University, China, in 2008, where he is currently pursuing the Ph.D. degree in signal and information processing. He is also a Researcher with the Ningbo Key Laboratory of Intelligent Household Appliances, Zhejiang Business Technology Institute, China. His research interests mainly include low complexity video processing and omni-vision quality assessment.

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Local Feature Descriptor and Derivative Filters for Blind Image Quality Assessment

Cited by 24 publications

References 40 publications

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

No‐reference image quality assessment of magnetic resonance images with high‐boost filtering and local features

No-Reference Image Quality Assessment Based on Multi-Task Generative Adversarial Network

Segmented Spherical Projection-Based Blind Omnidirectional Image Quality Assessment

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