Quality Assessment Framework Research Articles

An image response framework for no-reference image quality assessment

This paper proposes an image response framework for no-reference image quality assessment (NR IQA). The framework is an image quality feature extension framework, extending existing image quality features to new NR image quality features. In the framework, a test image is transformed into a number of sub-images through multi-scale preprocessing. The sub-images are taken as image objects, and each object is exerted with multiple local convolution operations as image inputs. Object responses are extracted from the objects under the inputs based on existing image quality features. All the responses compose a global image response feature vector and are mapped to a quality index. Object responses are the NR extensions of the existing image quality features, which signifies the extensions of IQA approaches. Experiments show that the framework can extend full-reference IQA and reduced-reference IQA approaches to NR IQA approaches, and extend NR IQA approaches to new higher-performance NR IQA approaches.

Use of Third-Party Indicators in Fund Reports

Fund staff use indicators developed by other organizations as input into analysis in surveillance and, to a lesser extent, in program work. While the Fund has been able to rely on data and statistics provided by member countries and compiled internally, continued efforts to foster global economic and financial stability require staff to work with indicators drawn from numerous third-party compilers. These indicators of varied qualities are used to measure concepts such as business environment, competitiveness, and quality of governance. It is anticipated that staff will continue to draw on other institutions’ expertise and estimates. This practice is consistent with the Executive Board’s guidance in areas where internal expertise is lacking or limited. It also puts a premium on staff’s understanding of the third-party indicators (TPIs) used to add analytical value, avoid flawed conclusions and presentation, and support traction with the membership. This paper outlines a framework to promote best practice with respect to use of TPIs in Fund reports. The framework will apply to all documents that are subject to the Fund’s Transparency Policy. Staff are encouraged to follow similar guidelines for other Fund documents. It draws on lessons from the current practice in the Fund and other selected international organizations (IOs), and insights from the application of an adapted data quality assessment framework (DQAF) to a subset of TPIs commonly used by Fund staff. Common good practices across IOs include the emphasis on staff judgment, review, and consultation with stakeholders.

Quantification of GABA, glutamate and glutamine in a single measurement at 3T using GABA-edited MEGA-PRESS.

γ‐Aminobutyric acid (GABA) and glutamate (Glu), major neurotransmitters in the brain, are recycled through glutamine (Gln). All three metabolites can be measured by magnetic resonance spectroscopy in vivo, although GABA measurement at 3 T requires an extra editing acquisition, such as Mescher–Garwood point‐resolved spectroscopy (MEGA‐PRESS). In a GABA‐edited MEGA‐PRESS spectrum, Glu and Gln co‐edit with GABA, providing the possibility to measure all three in one acquisition. In this study, we investigated the reliability of the composite Glu + Gln (Glx) peak estimation and the possibility of Glu and Gln separation in GABA‐edited MEGA‐PRESS spectra. The data acquired in vivo were used to develop a quality assessment framework which identified MEGA‐PRESS spectra in which Glu and Gln could be estimated reliably. Phantoms containing Glu, Gln, GABA and N‐acetylaspartate (NAA) at different concentrations were scanned using GABA‐edited MEGA‐PRESS at 3 T. Fifty‐six sets of spectra in five brain regions were acquired from 36 healthy volunteers. Based on the Glu/Gln ratio, data were classified as either within or outside the physiological range. A peak‐by‐peak quality assessment was performed on all data to investigate whether quality metrics can discriminate between these two classes of spectra. The quality metrics were as follows: the GABA signal‐to‐noise ratio, the NAA linewidth and the Glx Cramer–Rao lower bound (CRLB). The Glu and Gln concentrations were estimated with precision across all phantoms with a linear relationship between the measured and true concentrations: R 1 = 0.95 for Glu and R 1 = 0.91 for Gln. A quality assessment framework was set based on the criteria necessary for a good GABA‐edited MEGA‐PRESS spectrum. Simultaneous criteria of NAA linewidth <8 Hz and Glx CRLB <16% were defined as optimum features for reliable Glu and Gln quantification. Glu and Gln can be reliably quantified from GABA‐edited MEGA‐PRESS acquisitions. However, this reliability should be controlled using the quality assessment methods suggested in this work.

Visual Importance and Distortion Guided Deep Image Quality Assessment Framework

In this paper, we tackle the problem of no-reference image quality assessment (IQA). A learning-based IQA framework “VIDGIQA” is proposed, which extracts quality features from the input image and regresses the visual quality on these features. Since different distortions lead to different visual perceptions in the human visual system, distortion information is adopted to guide the feature learning process together with the human quality scores. Besides, a regression method is proposed to model and estimate the visual importance weights of all local regions, which can effectively improve the performance. More importantly, all these operations are integrated into one deep neural network, so that they can be jointly optimized and well cooperate with each other. Experiments were conducted to demonstrate the power of the proposed method on several datasets, including the LIVE dataset [1], the TID 2013 dataset [2], the LIVE multiply distorted IQA dataset [3], CSIQ [4] , and the LIVE in the wild image quality database [5]. The proposed method achieves 0.969 and 0.973 on the LIVE dataset [1] in terms of the spearman rank-order correlation coefficient and the Pearson linear correlation coefficient, respectively, which outperforms the state-of-the-art methods.

PATCH-IQ: A patch based learning framework for blind image quality assessment

Most well-known blind image quality assessment (BIQA) models usually follow a two-stage framework whereby various types of features are first extracted and used as an input to a regressor. The regression algorithm is used to model human perceptual measures based on a training set of distorted images. However, this approach requires an intensive training phase to optimise the regression parameters. In this paper, we overcome this limitation by proposing an alternative BIQA model that predicts image quality using nearest neighbour methods which have virtually zero training cost. The model, termed PATCH based blind Image Quality assessment (PATCH-IQ), has a learning framework that operates at the patch level. This enables PATCH-IQ to provide not only a global image quality estimation but also a local image quality estimation. Based on the assumption that the perceived quality of a distorted image will be best predicted by features drawn from images with the same distortion class, PATCH-IQ also introduces a distortion identification stage in its framework. This enables PATCH-IQ to identify the distortion affecting the image, a property that can be useful for further local processing stages. PATCH-IQ is evaluated on the standard IQA databases, and the provided scores are highly correlated to human perception of image quality. It also delivers competitive prediction accuracy and computational performance in relationship to other state-of-the-art BIQA models.

Photo Aesthetics Analysis via DCNN Feature Encoding

We propose an automatic framework for quality assessment of a photograph as well as analysis of its aesthetic attributes. In contrast to the previous methods that rely on manually designed features to account for photo aesthetics, our method automatically extracts such features using a pretrained deep convolutional neural network (DCNN). To make the DCNN-extracted features more suited to our target tasks of photo quality assessment and aesthetic attribute analysis, we propose a novel feature encoding scheme, which supports vector machines-driven sparse restricted Boltzmann machines, which enhances sparseness of features and discrimination between target classes. Experimental results show that our method outperforms the current state-of-the-art methods in automatic photo quality assessment, and gives aesthetic attribute ratings that can be used for photo editing. We demonstrate that our feature encoding scheme can also be applied to general object classification task to achieve performance gains.

Information Quality Assessment for Facility Management

Assessing the quality of building information models (BIMs) is an important yet challenging task within the construction industry as projects are increasingly being delivered with BIM. This is particularly essential for facility management (FM) users as downstream information consumers that depend on the quality of models developed in the previous project phases. The research presented in this paper addresses this challenge by introducing a framework for information quality assessment (IQA) of BIMs for FM uses. The IQA framework is the outcome of an extensive study of two large owner organizations involving numerous BIM projects. The framework is structured based on the essential FM subjects: assets, spaces, and systems, and the model characteristics: objects, attributes, relationships, and spatial information. The framework is then operationalized through the development and evaluation of information quality (IQ) tests using BIM model checking tools across three projects with different levels of detail and complexity. The proposed IQA framework and associated tests advance the state of knowledge about BIM quality in terms of methods to represent and evaluate conformance to owner requirements.

Quality Assessment Framework for Mobile Health Systems

Web-based, online social networking has received much attention from researchers in a variety of academic disciplines, with interesting issues addressed from different perspectives. Although Internet technologies play an instrumental role in the provision of the connectivity and message exchange capabilities that are required for the member interactions, the impacts of extensive usage of online social networks often are often on the non-technological aspects. This research investigates the driving forces of website loyalty, an issue of interest to both the individuals using the service for socializing and for the businesses deploying social networks as a serious technological tool for business promotion purposes. Using Facebook as the target of observation, this study evaluates the effects of social presence and social capital on website loyalty. The analysis reveals a positive influence of social presence on all three (structural, relational and cognitive) dimensions of social capital. Further, both the relational and cognitive dimensions of social capital have positive influence on the website loyalty.

A conceptual framework for quality assessment and management of biodiversity data.

The increasing availability of digitized biodiversity data worldwide, provided by an increasing number of institutions and researchers, and the growing use of those data for a variety of purposes have raised concerns related to the "fitness for use" of such data and the impact of data quality (DQ) on the outcomes of analyses, reports, and decisions. A consistent approach to assess and manage data quality is currently critical for biodiversity data users. However, achieving this goal has been particularly challenging because of idiosyncrasies inherent in the concept of quality. DQ assessment and management cannot be performed if we have not clearly established the quality needs from a data user’s standpoint. This paper defines a formal conceptual framework to support the biodiversity informatics community allowing for the description of the meaning of "fitness for use" from a data user’s perspective in a common and standardized manner. This proposed framework defines nine concepts organized into three classes: DQ Needs, DQ Solutions and DQ Report. The framework is intended to formalize human thinking into well-defined components to make it possible to share and reuse concepts of DQ needs, solutions and reports in a common way among user communities. With this framework, we establish a common ground for the collaborative development of solutions for DQ assessment and management based on data fitness for use principles. To validate the framework, we present a proof of concept based on a case study at the Museum of Comparative Zoology of Harvard University. In future work, we will use the framework to engage the biodiversity informatics community to formalize and share DQ profiles related to DQ needs across the community.

QATCH - An adaptive framework for software product quality assessment

The subjectivity that underlies the notion of quality does not allow the design and development of a universally accepted mechanism for software quality assessment. This is why contemporary research is now focused on seeking mechanisms able to produce software quality models that can be easily adjusted to custom user needs. In this context, we introduce QATCH, an integrated framework that applies static analysis to benchmark repositories in order to generate software quality models tailored to stakeholder specifications. Fuzzy multi-criteria decision-making is employed in order to model the uncertainty imposed by experts’ judgments. These judgments can be expressed into linguistic values, which makes the process more intuitive. Furthermore, a robust software quality model, the base model, is generated by the system, which is used in the experiments for QATCH system verification. The paper provides an extensive analysis of QATCH and thoroughly discusses its validity and added value in the field of software quality through a number of individual experiments.

Learning quality assessment of retargeted images

Content-aware image resizing (or image retargeting) enables images to be fit to different display devices having different aspect ratios while preserving salient image content. There are many approaches to retargeting, although no “best” method has been agreed upon. Therefore, finding ways to assess the quality of image retargeting has become a prominent challenge. Traditional image quality assessment methods are not directly applicable to image retargeting because the retargeted image size is not same as the original one. In this paper, we propose an open framework for image retargeting quality assessment, where the quality prediction engine is a trained Radial Basis Function (RBF) neural network. Broadly, our approach is motivated by the observation that no single method can be expected to perform well on all types of content. We train the network on ten perceptually relevant features, including a saliency-weighted, SIFT-directed complex wavelet structural similarity (CW-SSIM) index, and a new image aesthetics evaluation method. These two features and eight other features are used by the neural network to learn to assess the quality of retargeted images. The accuracy of the new model is extensively verified by simulations.

Linking open data and the crowd for real-time passenger information

The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system.

A hybrid learning-based framework for blind image quality assessment

Blind image quality assessment aims to evaluate the quality of a given image without the availability of the original ground truth image and without the prior knowledge of the types of distortions present. Instead of using hand-crafted features to address specific type of image distortions, a hybrid learning-based blind image quality assessment approach is proposed in this paper to address more challenging mixed types of image distortions. The proposed approach integrates the convolution neural network (CNN) as a feature extractor, plus the support vector regression method to learn a mapping function from the CNN-trained features to the quality score of the input image. Extensive experiments are conducted using both standard image dataset and real-world surveillance video dataset to demonstrate the superior performance of the proposed approach.

Nursing Students' Perceptions of the Objective Structured Clinical Examination: An Integrative Review

BackgroundObjective structured clinical examinations (OSCE) are commonly used to assess the clinical competence of undergraduate nursing students. There are, however, concerns about the stresses involved in such examinations and the perceived validity of such exams from a student perspective. Without student engagement the authenticity of such assessment may be compromised. Therefore, the aim of this integrative review was to explore and evaluate literature around undergraduate nursing students’ perceptions of OCSEs and summarise key themes emerging from this literature to establish current knowledge and understanding. This exploration of the literature aims to identify areas lacking quality research and make recommendations for future research. MethodsTo undertake this review, we searched four databases from 1988-2015 using a range of keyword combinations relating to student perception and OSCEs. We also undertook reference chaining. Inclusion/exclusion criteria were applied to abstracts and then full papers. Quality was assessed by consensus between two authors using the National Health and Medical Research Council (NH&MRC) quality assessment framework and the mixed-methods assessment tool. ResultsThirteen studies met the inclusion criteria, and three primary domains emerged from data analysis: These domains, identified from the thematic analysis of these studies in relation to students perceptions of OSCEs include preparation for OSCE, process of the OSCE and performance following the OSCE. ConclusionsFrom this review we can conclude that academics/faculties must carefully consider OCSE preparation, process and follow-up when planning OCSEs for assessments including associated resource implications. OSCEs are used internationally and yet there is no validated, reliable tool for student perception that could be applied across facilities. Student engagement in the OSCE is essential for successful OCSEs and yet this consideration appeared rarely in the literature. A number of recommendations emerged from the review including the need for an evidence-based form of assessment that has a strong underpinning pedagogy.

Using digital interventions to improve the cardiometabolic health of populations: a meta-review of reporting quality.

We conducted a meta-review to determine the reporting quality of user-centered digital interventions for the prevention and management of cardiometabolic conditions. Using predetermined inclusion criteria, systematic reviews published between 2010 and 2015 were identified from 3 databases. To assess whether current evidence is sufficient to inform wider uptake and implementation of digital health programs, we assessed the quality of reporting of research findings using (1) endorsement of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, (2) a quality assessment framework (eg, Cochrane risk of bias assessment tool), and (3) 8 parameters of the Consolidated Standards of Reporting Trials of Electronic and Mobile HEalth Applications and onLine TeleHealth (CONSORT-eHEALTH) guidelines (developed in 2010). Of the 33 systematic reviews covering social media, Web-based programs, mobile health programs, and composite modalities, 6 reported using the recommended PRISMA guidelines. Seven did not report using a quality assessment framework. Applying the CONSORT-EHEALTH guidelines, reporting was of mild to moderate strength. To our knowledge, this is the first meta-review to provide a comprehensive analysis of the quality of reporting of research findings for a range of digital health interventions. Our findings suggest that the evidence base and quality of reporting in this rapidly developing field needs significant improvement in order to inform wider implementation and uptake. The inconsistent quality of reporting of digital health interventions for cardiometabolic outcomes may be a critical impediment to real-world implementation.

Quality Index for Stereoscopic Images by Jointly Evaluating Cyclopean Amplitude and Cyclopean Phase

With widespread applications of three-dimensional (3-D) technology, measuring quality of experience for 3-D multimedia content plays an increasingly important role. In this paper, we propose a full reference stereo image quality assessment (SIQA) framework which focuses on the innovation of binocular visual properties and applications of low-level features. On one hand, based on the fact that human visual system understands an image mainly according to its low-level features, local phase and local amplitude extracted from phase congruency measurement are employed as primary features. Considering the less prominent performance of amplitude in IQA, visual saliency is applied into the modification on amplitude. On the other hand, by fully considering binocular rivalry phenomena, we create the cyclopean amplitude map and cyclopean phase map. With this method, both image features and binocular visual properties are mutually combined with each other. Meanwhile, a novel binocular modulation function in spatial domain is also adopted into the overall quality prediction of amplitude and phase. Extensive experiments demonstrate that the proposed framework achieves higher consistency with subjective tests than relevant SIQA metrics.

Linking Open Data and the Crowd for Real-Time Passenger Information

The availability of real-time passenger information (RTPI) is a key factor in making public transport both accessible and attractive to users. Unfortunately, rural areas often lack the infrastructure necessary to provide such information, and the cost of deploying and maintaining the required technologies outside of urban areas is seen as prohibitive. In this paper we present the GetThere system developed to overcome such issues and to provide public transport users in rural areas with RTPI. An ontological framework for representing mobility information is described, along with the Linked Data approach used to integrate heterogeneous data from multiple sources including government, transport operators, and the public. To mitigate possible issues with the veracity of this data, a quality assessment framework was developed that utilises data provenance. We also discuss our experiences working with Semantic Web technologies in this domain, and present results from both a user trial and a performance evaluation of the system.

Towards a simulation-based framework for decision support in healthcare quality assessment

This article describes a simulation framework for healthcare quality assessment from the perspective of management and corresponding decision-makers. The proposed framework will allow simulating “what-if” scenarios and getting alternative outcomes in case of decision support systems. In our research, we are dealing with heterogeneous data sources, which combine within data-flow processes. The data flow of the presented framework conceptually combines several modeling methods: discrete-event simulation, agent-based modeling and also includes data analysis. The experiments were executed based on data from the Almazov National Medical Research Centre hospital information and access control systems.

Generalized referenceless image quality assessment framework using texture energy measures and pattern strength features

Referenceless image quality assessment is a challenging and critical problem in today's multimedia applica\-tions. Texture patterns in images are normally at high frequencies compared to lower ones. Due to the effect of distortions during acquisition, compression, and transmission, texture deviation artifacts are generated that cause a granular effect in the image. Other artifacts, such as blocking, affect high frequencies in an image, causing distorted edges. Combining the analysis of texture deviation and other artifacts helps in determining the quality of an image. The proposed approach uses variation in the energy of pixels to quantify the quality of an image. These variations are calculated using texture energy measures and pattern strength-based statistical features. In the proposed approach, machine learning-based classifiers are used to predict the quality score for an image. The performance of the proposed method is tested for all images ranging from pristine to poor quality from LIVE and TID2008 databases. For different distortions, results are shown to have good correlation if they lie between the predicted score and the differential mean opinion score. Results obtained with this approach are compared with other widely used referenceless approaches. It is observed that the proposed approach shows better performance in the quantification of the quality of an image.

A New Framework for Quality Assessment of High-Resolution Fingerprint Images

The quality assessment of sets of features extracted from patterns of epidermal ridges on our fingers is a biometric challenge problem with implications on questions concerning security, privacy and identity fraud. In this work, we introduced a new methodology to analyze the quality of high-resolution fingerprint images containing sets of fingerprint pores. Our approach takes into account the spatial interrelationship between the considered features and some basic transformations involving point process and anisotropic analysis. We proposed two new quality index algorithms following spatial and structural classes of analysis. These algorithms have proved to be effective as a performance predictor and as a filter excluding low-quality features in a recognition process. The experiments using error reject curves show that the proposed approaches outperform the state-of-the-art quality assessment algorithm for high-resolution fingerprint recognition, besides defining a new method for reconstructing their friction ridge phases in a very consistent way.