No-Reference Image Quality Assessment Using Image Statistics and Robust Feature Descriptors

Oszust, Mariusz

doi:10.1109/lsp.2017.2754539

Cited by 28 publications

(24 citation statements)

References 37 publications

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“…They also used LBP extracted from texture and structural maps [38]. A more advanced gradient-based image descriptor, Speeded-Up Robust Features (SURF), is employed in the measure proposed by Oszust [27]. In that work, the sample mean, standard deviation, entropy, skewness, kurtosis, and histogram variance for the assessed image, the image filtered with Prewitt operators, and their SURF features are used.…”

Section: Related Workmentioning

confidence: 99%

“…The nine HOG descriptors presented in Figure 6a were selected to be used jointly. In the case of single application of the descriptor D(C 1×1 , B 1×1 ), the method resembles approaches in which statistics for a gradient map, among other features, are used for the quality prediction (e.g., [27]). To show that such joint application of the HOG descriptors is beneficial, Figure 6b reports the performance of the method with 1, 2, .…”

Section: Metric Configuration and Contribution Of Featuresmentioning

confidence: 99%

“…The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.Symmetry 2019, 11, 95 2 of 20 patches [28] can be found in the literature.Since the supervised learning bridges image statistics with the perceptual quality, it is often applied to obtain a model used for the quality prediction. For the learning, the Support Vector Regression (SVR) [20,21,23,27], neural networks [24], or random forests [29] are applied. In methods that do not use supervised learning, distortion types are modeled with a set of centroids of quality levels or NSS from multiple cues [30,31].…”

mentioning

confidence: 99%

“…However, a spatial distribution of LBP may not be able to capture more complex structures [37]. Thus, statistics extracted from gradient maps often occur in conjunction with other approaches to improve the IQA performance [27,38]. Such techniques use global distributions of gradient magnitude maps [25], relative gradient orientations or magnitude [24,39].To describe an image and efficiently take into account local gradient orientations, Histogram of Oriented Gradients (HOG) descriptor can be used [40].…”

mentioning

confidence: 99%

“…A variety of gradient-based features are often employed to model NSS [24]. Furthermore, the use of perceptual features [25][26][27] or image…”

mentioning

confidence: 99%

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No-Reference Image Quality Assessment with Local Gradient Orientations

Oszust

2019

Symmetry

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Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.Symmetry 2019, 11, 95 2 of 20 patches [28] can be found in the literature.Since the supervised learning bridges image statistics with the perceptual quality, it is often applied to obtain a model used for the quality prediction. For the learning, the Support Vector Regression (SVR) [20,21,23,27], neural networks [24], or random forests [29] are applied. In methods that do not use supervised learning, distortion types are modeled with a set of centroids of quality levels or NSS from multiple cues [30,31]. Another direction is to employ a pseudo-reference image which is created and compared with a distorted image with blockiness, sharpness, and noisiness metrics [32]. Recently, many NR-IQA approaches which use deep neural network (DNN) architectures have been introduced. They merge the feature extraction and quality prediction steps. However, they suffer from a small number of training examples available in IQA benchmark datasets or use complex architectures that are devoted to image recognition tasks. To overcome these limitations, most of them use image patches [33,34], train models using FR-IQA measures instead of subjective scores [34,35], or perform fine-tuning to adopt an architecture to the IQA [36]. Interestingly, some DNN-based approaches use features introduced in earlier methods [35].The HVS is sensitive to local structures, which are often described using local binary patterns (LBP) and gradient-based statistics. However, a spatial distribution of LBP may not be able to capture more complex structures [37]. Thus, statistics extracted from gradient maps often occur in conjunction with other approaches to improve the IQA performance [27,38]. Such techniques use global distributions of gradient magnitude maps [25], relative gradient orientations or magnitude [24,39].To describe an image and efficiently take into account local gradient orientations, Histogram of Oriented Gradients (HOG) descriptor can be used [40]. However, the HOG produces high-dimensional feature vectors, which are devoted to object recognition...

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Section: Related Workmentioning

confidence: 99%

Section: Metric Configuration and Contribution Of Featuresmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…A variety of gradient-based features are often employed to model NSS [24]. Furthermore, the use of perceptual features [25][26][27] or image…”

mentioning

confidence: 99%

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No-Reference Image Quality Assessment with Local Gradient Orientations

Oszust

2019

Symmetry

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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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