Single Quality Score Research Articles

High Dynamic Range Image Quality Assessment Based on Frequency Disparity

In this paper, a novel and effective image quality assessment (IQA) algorithm based on frequency disparity for high dynamic range (HDR) images is proposed, termed as local-global frequency feature-based model (LGFM). Motivated by the assumption that the human visual system (HVS) is highly adapted for extracting structural information and partial frequencies when perceiving the visual scene, the Gabor and the Butterworth filters are applied to the luminance component of the HDR image to extract the local and global frequency features, respectively. The similarity measurement and feature pooling strategy are sequentially performed on the frequency features to obtain the predicted single quality score. The experiments evaluated on four widely used benchmarks demonstrate that the proposed LGFM can provide a higher consistency with the subjective perception compared with the state-of-the-art HDR IQA methods. Our code is available at: https://github.com/eezkni/LGFM.

Toward a No-Reference Quality Metric for Camera-Captured Images.

Existing no-reference (NR) image quality assessment (IQA) metrics are still not convincing for evaluating the quality of the camera-captured images. Toward tackling this issue, we, in this article, establish a novel NR quality metric for quantifying the quality of the camera-captured images reliably. Since the image quality is hierarchically perceived from the low-level preliminary visual perception to the high-level semantic comprehension in the human brain, in our proposed metric, we characterize the image quality by exploiting both the low-level image properties and the high-level semantics of the image. Specifically, we extract a series of low-level features to characterize the fundamental image properties, including the brightness, saturation, contrast, noiseness, sharpness, and naturalness, which are highly indicative of the camera-captured image quality. Correspondingly, the high-level features are designed to characterize the semantics of the image. The low-level and high-level perceptual features play complementary roles in measuring the image quality. To infer the image quality, we employ the support vector regression (SVR) to map all the informative features to a single quality score. Thorough tests conducted on two standard camera-captured image databases demonstrate the effectiveness of the proposed quality metric in assessing the image quality and its superiority over the state-of-the-art NR quality metrics. The source code of the proposed metric for camera-captured images is released at https://github.com/YT2015?tab=repositories.

Study of Spatio-Temporal Modeling in Video Quality Assessment.

Video quality assessment (VQA) has received remarkable attention recently. Most of the popular VQA models employ recurrent neural networks (RNNs) to capture the temporal quality variation of videos. However, each long-term video sequence is commonly labeled with a single quality score, with which RNNs might not be able to learn long-term quality variation well: What's the real role of RNNs in learning the visual quality of videos? Does it learn spatio-temporal representation as expected or just aggregating spatial features redundantly? In this study, we conduct a comprehensive study by training a family of VQA models with carefully designed frame sampling strategies and spatio-temporal fusion methods. Our extensive experiments on four publicly available in- the-wild video quality datasets lead to two main findings. First, the plausible spatio-temporal modeling module (i. e., RNNs) does not facilitate quality-aware spatio-temporal feature learning. Second, sparsely sampled video frames are capable of obtaining the competitive performance against using all video frames as the input. In other words, spatial features play a vital role in capturing video quality variation for VQA. To our best knowledge, this is the first work to explore the issue of spatio-temporal modeling in VQA.

A New Longevity Design Methodology Based on Consumer-Oriented Quality for Fashion Products

Design for longevity is known as an eco-design opportunity and could help to reduce the environmental footprint of energy-free items. However, extending the lifespan of products is not always desirable and the focus should be on achieving an optimal lifespan. Operationally, recommendations for design for longevity usually refer to durability, repairability, upgradability or emotional attachment. The use of high-quality and robust material is frequently stated, although it is not obvious what high-quality material is. Based on a quality by design approach, this study aims to propose a methodology to design for optimal longevity with a consumer-oriented approach. To do so, it includes data collection of product quality and manufacturing processes and then embeds consumers’ knowledge. These are combined into data analysis to help to highlight relationships and the most appropriate quality contributors. This methodology relies on three-steps: first, a single quality score which includes consumers’ knowledge; secondly, a multi-scale reverse-engineering process; and finally a data analysis using principal component analysis. The originality of such a proposal is that it enables the consumers’ knowledge to be considered in the identification of appropriated quality contributors. The proposed methodology is implemented in the fashion sector as it is said to be the second most polluting one. Moreover, given the huge variety of materials and production processes available in textiles, the selection of the most suitable recommendations to support a longer lifespan is very complex. The presented case study involves 29 T-shirts and reveals the mechanical-related strengths to be the main quality contributors.

Inter-reader agreement of the PI-QUAL score for prostate MRI quality in the NeuroSAFE PROOF trial

ObjectivesThe Prostate Imaging Quality (PI-QUAL) score assesses the quality of multiparametric MRI (mpMRI). A score of 1 means all sequences are below the minimum standard of diagnostic quality, 3 implies that the scan is of sufficient diagnostic quality, and 5 means that all three sequences are of optimal diagnostic quality. We investigated the inter-reader reproducibility of the PI-QUAL score in patients enrolled in the NeuroSAFE PROOF trial.MethodsWe analysed the scans of 103 patients on different MR systems and vendors from 12 different hospitals. Two dedicated radiologists highly experienced in prostate mpMRI independently assessed the PI-QUAL score for each scan. Interobserver agreement was assessed using Cohen’s kappa with standard quadratic weighting (κw) and percent agreement.ResultsThe agreement for each single PI-QUAL score was strong (κw = 0.85 and percent agreement = 84%). A similar agreement (κw = 0.82 and percent agreement = 84%) was observed when the scans were clustered into three groups (PI-QUAL 1–2 vs PI-QUAL 3 vs PI-QUAL 4–5). The agreement in terms of diagnostic quality for each single sequence was highest for T2-weighted imaging (92/103 scans; 89%), followed by dynamic contrast-enhanced sequences (91/103; 88%) and diffusion-weighted imaging (80/103; 78%).ConclusionWe observed strong reproducibility in the assessment of PI-QUAL between two radiologists with high expertise in prostate mpMRI. At present, PI-QUAL offers clinicians the only available tool for evaluating and reporting the quality of prostate mpMRI in a systematic manner but further refinements of this scoring system are warranted.Key Points• Inter-reader agreement for each single Prostate Imaging Quality (PI-QUAL) score (i.e., PI-QUAL 1 to PI-QUAL 5) was strong, with weighted kappa = 0.85 (95% confidence intervals: 0.51 – 1) and percent agreement = 84%.• Interobserver agreement was strong when the scans were clustered into three groups according to the ability (or not) to rule in and to rule out clinically significant prostate cancer (i.e., PI-QUAL 1-2 vs PI-QUAL 3 vs PI-QUAL 4–5), with weighted kappa = 0.82 (95% confidence intervals: 0.68 – 0.96) and percent agreement = 84%.• T2-weighted acquisitions were the most compliant with the Prostate Imaging Reporting and Data System (PI-RADS) v. 2.0 technical recommendations and were the sequences of highest diagnostic quality for both readers in 95/103 (92%) scans, followed by dynamic contrast enhanced acquisition with 81/103 (79%) scans and lastly by diffusion-weighted imaging with 79/103 (77%) scans.

A New Approach to Identifying Top-Priority Step for Increasing the Building Materials Competitiveness

The problem of development the competitiveness of building products in the condition of limited financial resources is discussed. It has been proposed a new methodological approach that allows manufacturers to determine the priority direction of development of product quality while they maintains or slightly increases the selling price of products. The new methodological approach is based on a few steps including assessment of the properties significance for the consumers through an individual survey of experts and calculation of a consumer r property significance ratio; the formation of the virtual standard-product parameters; calculation of the relative Single Quality Score for every property which based on comparison the actual property of product with property of virtual standard-product. The identification of this priority direction of the product’s development should be carried out on the basis of an internal audit of the industrial company. The results of the audit would determine the reserves of improving technology parameters and product quality. As a result of the identified internal reserves, top management of an industrial company can operate a plan to improve product’s quality, the implementation a schedule of this plan should be built on a parallel-sequential principle.

Blind image quality assessment based on the multiscale and dual‐domains features fusion

AbstractImage quality assessment is to simulate subjective human visual perception and realize image quality inference automatically. Although deep neural networks have achieved great success, the majority of them do not fully consider perception characteristics. Therefore, according to the human visual scale characteristics, we proposed an image quality assessment algorithm based on multiscale and dual domains fusion. Firstly, the original image and its phase congruency respectively input into two branches, feature pyramid and channel attention mechanism are adopted to extract multiscale features. After that, bilinear pool is used to aggregate the spatial and frequency domain characteristics of the corresponding scales, and allows arbitrary scale input to ensure that the features are extracted from the inherent quality images. Finally, the single quality score is obtained through learned weights of each scale. Comparative experiments between our approach and state‐of‐the‐art are conducted on five public databases, the results demonstrate that the proposed algorithm is not only robust to different types and across database, but also sensitive to scale.

A Layered Approach for Quality Assessment of DIBR‐Synthesized Images

Multiview video plus depth (MVD) is a popular video format that supports three‐dimensional television (3DTV) and free viewpoint television (FTV). 3DTV and FTV provide depth sensation to the viewer by presenting two views of the same scene but with slightly different angles. In MVD, few views are captured, and each view has the color image and the corresponding depth map which is used in depth image‐based rendering (DIBR) to generate views at novel viewpoints. The DIBR can introduce various artifacts in the synthesized view resulting in poor quality. Therefore, evaluating the quality of the synthesized image is crucial to provide an appreciable quality of experience (QoE) to the viewer. In a 3D scene, objects are at a different distance from the camera, characterized by their depth. In this paper, we investigate the effect that objects at a different distance make on the overall QoE. In particular, we find that the quality of the closer objects contributes more to the overall quality as compared to the background objects. Based on this phenomenon, we propose a 3D quality assessment metric to evaluate the quality of the synthesized images. The proposed metric using the depth of the scene divides the image into different layers where each layer represents the objects at a different distance from the camera. The quality of each layer is individually computed, and their scores are pooled together to obtain a single quality score that represents the quality of the synthesized image. The performance of the proposed metric is evaluated on two benchmark DIBR image databases. The results show that the proposed metric is highly accurate and performs better than most existing 2D and 3D quality assessment algorithms.

Region-based image segmentation evaluation via perceptual pooling strategies

Image segmentation is an essential step for many computer vision tasks. Evaluating the quality of image segmentations becomes indispensable for choosing an appropriate output of the image segmentation algorithms. To quantitatively evaluate the segmentation quality, various evaluation measures have been proposed to produce a quality map, and a spatial pooling algorithm is followed to combine the quality map into a single quality score. In this paper, we propose two pooling strategies instead of using the conventional spatial average operation. By assigning perceptual meaningful weights to the quality maps, we obtain evaluation measures that are correlated with the human perception of segmentation quality. Specifically, a quality-based and a visual importance-based pooling strategies are designed and tested on some popular evaluation measures, respectively. To the best of our knowledge, this is the first work that applies perceptual pooling strategies for segmentation evaluation. Extensive experiments are conducted on a subjective evaluation benchmark and the Berkeley Segmentation Dataset (BSDS500). The results indicate that the proposed strategies can improve the performance of existing evaluation measures and produce a more perceptually meaningful judgment on the segmentation quality.

Untrained consumer assessment of the eating quality of European beef: 2. Demographic factors have only minor effects on consumer scores and willingness to pay

The beef industry must become more responsive to the changing market place and consumer demands. An essential part of this is quantifying a consumer’s perception of the eating quality of beef and their willingness to pay for that quality, across a broad range of demographics. Over 19 000 consumers from Northern Ireland, Poland, Ireland and France each tasted seven beef samples and scored them for tenderness, juiciness, flavour liking and overall liking. These scores were weighted and combined to create a fifth score, termed the Meat Quality 4 score (MQ4) (0.3×tenderness, 0.1×juiciness, 0.3×flavour liking and 0.3×overall liking). They also allocated the beef samples into one of four quality grades that best described the sample; unsatisfactory, good-every-day, better-than-every-day or premium. After the completion of the tasting panel, consumers were then asked to detail, in their own currency, their willingness to pay for these four categories which was subsequently converted to a proportion relative to the good-every-day category (P-WTP). Consumers also answered a short demographic questionnaire. The four sensory scores, the MQ4 score and the P-WTP were analysed separately, as dependant variables in linear mixed effects models. The answers from the demographic questionnaire were included in the model as fixed effects. Overall, there were only small differences in consumer scores and P-WTP between demographic groups. Consumers who preferred their beef cooked medium or well-done scored beef higher, except in Poland, where the opposite trend was found. This may be because Polish consumers were more likely to prefer their beef cooked well-done, but samples were cooked medium for this group. There was a small positive relationship with the importance of beef in the diet, increasing sensory scores by about 4% in Poland and Northern Ireland. Men also scored beef about 2% higher than women for most sensory scores in most countries. In most countries, consumers were willing to pay between 150 and 200% more for premium beef, and there was a 50% penalty in value for unsatisfactory beef. After quality grade, by far the greatest influence on P-WTP was country of origin. Consumer age also had a small negative relationship with P-WTP. The results indicate that a single quality score could reliably describe the eating quality experienced by all consumers. In addition, if reliable quality information is delivered to consumers they will pay more for better quality beef, which would add value to the beef industry and encourage improvements in quality.

Image decomposition-based structural similarity index for image quality assessment

Perceptual image quality assessment (IQA) adopts a computational model to assess the image quality in a fashion, which is consistent with human visual system (HVS). From the view of HVS, different image regions have different importance. Based on this fact, we propose a simple and effective method based on the image decomposition for image quality assessment. In our method, we first divide an image into two components: edge component and texture component. To separate edge and texture components, we use the TV flow-based nonlinear diffusion method rather than the classic TV regularization methods, for highly effective computing. Different from the existing content-based IQA methods, we realize different methods on different components to compute image quality. More specifically, the luminance and contrast similarity are computed in texture component, while the structural similarity is computed in edge component. After obtaining the local quality map, we use texture component again as a weight function to derive a single quality score. Experimental results on five datasets show that, compared with previous approaches in the literatures, the proposed method is more efficient and delivers higher prediction accuracy.

An Objective Deghosting Quality Metric for HDR Images

AbstractReconstructing high dynamic range (HDR) images of a complex scene involving moving objects and dynamic backgrounds is prone to artifacts. A large number of methods have been proposed that attempt to alleviate these artifacts, known as HDR deghosting algorithms. Currently, the quality of these algorithms are judged by subjective evaluations, which are tedious to conduct and get quickly outdated as new algorithms are proposed on a rapid basis. In this paper, we propose an objective metric which aims to simplify this process. Our metric takes a stack of input exposures and the deghosting result and produces a set of artifact maps for different types of artifacts. These artifact maps can be combined to yield a single quality score. We performed a subjective experiment involving 52 subjects and 16 different scenes to validate the agreement of our quality scores with subjective judgements and observed a concordance of almost 80%. Our metric also enables a novel application that we call as hybrid deghosting, in which the output of different deghosting algorithms are combined to obtain a superior deghosting result.

A Fast Feature Similarity Index for Image Quality Assessment

The main drawback of the phase congruency feature employed in the feature similarity index (FSIM) image quality assessment (IQA) algorithm is its low computational efficiency. In this paper, a novel fast feature similarity index (FFSIM) for image quality assessment is proposed. Based on the fact that human visual system (HVS) responds to the brightness stimulus mainly complying with Weber's law, the proposed FFSIM only performs spatial filtering to quickly calculate the contrast between the current pixel and its background, which is used to compute Weber visual salience similarity and a weighting coefficient in pooling stage after applied nonlinear mapping. Weber contrast and the gradient magnitude play complementary roles in characterizing the image local quality. After obtaining the local quality map, we use Weber weighting coefficient again as a weighting coefficient to derive a single quality score. As such, the multi-scale version of the FFSIM algorithm, i.e., MS-FFSIM is also proposed, which complies with the spatial frequency response characteristics of the HVS system. Extensive experiments performed on six publicly available IQA databases demonstrate that the proposed FFSIM and MS-FFSIM can achieve higher consistency with the subjective evaluations than state-of-the-art IQA metrics and the computational efficiency is greatly improved as well.

ProTSAV: A protein tertiary structure analysis and validation server

Quality assessment of predicted model structures of proteins is as important as the protein tertiary structure prediction. A highly efficient quality assessment of predicted model structures directs further research on function. Here we present a new server ProTSAV, capable of evaluating predicted model structures based on some popular online servers and standalone tools. ProTSAV furnishes the user with a single quality score in case of individual protein structure along with a graphical representation and ranking in case of multiple protein structure assessment. The server is validated on ~64,446 protein structures including experimental structures from RCSB and predicted model structures for CASP targets and from public decoy sets. ProTSAV succeeds in predicting quality of protein structures with a specificity of 100% and a sensitivity of 98% on experimentally solved structures and achieves a specificity of 88%and a sensitivity of 91% on predicted protein structures of CASP11 targets under 2Å.The server overcomes the limitations of any single server/method and is seen to be robust in helping in quality assessment.ProTSAV is freely available at http://www.scfbio-iitd.res.in/software/proteomics/protsav.jsp

Image quality assessment: A sparse learning way

Full reference image quality assessment is very important for many image processing applications. The challenge of image quality assessment lies in two aspects: (1) formulating perceptual meaningful features and (2) finding a way to pool them into a single quality score. A novel two-step approach is proposed to address these problems. In the first step, sparse representations of local image patches are computed to simulate the low level characteristic of the human vision system (HVS) and represent the meaningful image structures. The differences between the representations of distorted and undistorted patches are utilized to measure the local distortion. In the second step, these local distortion measurements are fused into a single image quality score by using kernel ridge regression (KRR). Kernel ridge regression can mimic the complex high level behaviors of human vision system and is shown to be an effective way to learn the relationship between local quality measurements and quality score. The contributions of this paper would be summarized as follows: (1) extracting approximate perceptual meaningful features in image quality assessment is transformed as a sparse representation problem. In this case, the sparse representation coefficients can reflect the salient local structures and give local quality assessments. (2) The KRR is utilized to pool the local quality assessments into a single image quality score. Thus, the nonlinear relationship between the objective model outputs and the subjective quality ratings can be learned by exploiting the KRR. (3) Extensive experiments are conducted on six public databases. Compared with other approaches, the proposed approach has achieved the best performance, which demonstrates the effectiveness and robustness of the proposed approach.

Monogenic signal theory based feature similarity index for image quality assessment

Image quality assessment (IQA) aims to establish generic metrics consistently with subjective evaluations using computational models. Recent phase congruency, which is a dimensionless, normalized feature of a local structure, is used as the structure similarity feature. This paper proposes a novel feature similarity index that is Riesz transform based monogenic phase congruency feature similarity index (RMFSIM). RMFSIM is based on monogenic signal theory for full reference IQA. Monogenic phase congruency (MPC) map, instead of phase congruency map, is constructed utilizing the local phase, the local orientation and the local energy information of the 2D monogenic signal. The corresponding 1st-order and 2nd-order coefficients of the MPC map are obtained by Riesz transform. The local feature coefficients similarities are computed by the similarity measure, and then a final single quality score is derived from weighting feature coefficients similarities. Experimental results demonstrate that the proposed similarity index is highly consistent with human subjective evaluations and achieves good performance with the existing state-of-the-art methods in terms of prediction monotonicity and accuracy.

Image quality assessment based on matching pursuit

The objective assessment of image quality is an essential part of many visual processing systems. The challenge lies in evaluating the image quality consistently under subjective perceptions. In this paper, we propose a novel image quality metric based on the matching pursuit algorithm. Under the principle of structural information distortion, we assume that various structure data contributes differently to the single image quality score. Specifically, we decompose the reference image using matching pursuit with a separable 2D Gabor dictionary, thus obtaining structural information, and develop a characterization for this information and its importance. We then discuss the relationship between the structural distortion intensity and the subjective quality measurement on the energy scale. In experiments, we compare the performance of our algorithm with both subjective ratings and state-of-the-art objective methods on image datasets with multi-type distortions. The experimental results validate our proposed method.

Image quality assessment using a SVD-based structural projection

The development of objective image quality assessment (IQA) metrics aligned with human perception is of fundamental importance to numerous image-processing applications. Recently, human visual system (HVS)-based engineering algorithms have received widespread attention for their low computational complexity and good performance. In this paper, we propose a new IQA model by incorporating these available engineering principles. A local singular value decomposition (SVD) is first utilised as a structural projection tool to select local image distortion features, and then, both perceptual spatial pooling and neural networks (NN) are employed to combine feature vectors to predict a single perceptual quality score. Extensive experiments and cross-validations conducted with three publicly available IQA databases demonstrate the accuracy, consistency, robustness, and stability of the proposed approach compared to state-of-the-art IQA methods, such as Visual Information Fidelity (VIF), Visual Signal to Noise Ratio (VSNR), and Structural Similarity Index (SSIM).

MLSIM: A Multi-Level Similarity index for image quality assessment

Image quality assessment (IQA) is of great importance to numerous image processing applications, and various methods have been proposed for it. In this paper, a Multi-Level Similarity (MLSIM) index for full reference IQA is proposed. The proposed metric is based on the fact that human visual system (HVS) distinguishes the quality of an image mainly according to the details given by low-level gradient information. In the proposed metric, the Prewitt operator is first utilized to get gradient information of both reference and distorted images, then the gradient information of reference image is segmented into three levels (3LSIM) or two levels (2LSIM), and the gradient information of distorted image is segmented by the corresponding regions of reference image, therefore we get multi-level information of these two images. Riesz transform is utilized to get corresponding features of different levels and the corresponding 1st-order and 2nd-order coefficients are combined together by regional mutual information (RMI) and weighted to obtain a single quality score. Experimental results demonstrate that the proposed metric is highly consistent with human subjective evaluations and achieves good performance.

Image quality assessment based on Prewitt magnitude

The goal of image quality assessment (IQA) research is to use computational models to calculate the quality of images consistently with subjective evaluations. In this paper, we propose a new image quality assessment (IQA) algorithm by combining Prewitt magnitude and regional mutual information (RMI) in HSV color space. The Prewitt operator is usually used for edge detection and can extract vertical edge more accurately than other operators. The HSV color space encapsulates information about a color in terms that are more natural and intuitive to humans. The proposed method PMRMI first transforms reference and distorted images from RGB color space into HSV color space and Prewitt magnitude is introduced to extract key edge features of each channel. Then the regional mutual information is calculated to measure the similarity of the two images. After that, a weighting method is utilized for better consistency with subjective evaluations. Therefore we get a single quality score. Experiments on various image distortion types demonstrate that the proposed algorithm can achieve better consistency with the subjective evaluations than PSNR and SSIM.