Image Quality Assessment Approach Research Articles

Model observer design for multi-signal detection in the presence of anatomical noise

As psychophysical studies are resource-intensive to conduct, model observers are commonly used to assess and optimize medical imaging quality. Model observers are typically designed to detect at most one signal. However, in clinical practice, there may be multiple abnormalities in a single image set (e.g. multifocal multicentric (MFMC) breast cancer), which can impact treatment planning. Prevalence of signals can be different across anatomical regions, and human observers do not know the number or location of signals a priori. As new imaging techniques have the potential to improve multiple-signal detection (e.g. digital breast tomosynthesis may be more effective for diagnosis of MFMC than mammography), image quality assessment approaches addressing such tasks are needed. In this study, we present a model observer to detect multiple signals in an image dataset. A novel implementation of partial least squares (PLS) was developed to estimate different sets of efficient channels directly from the images. The PLS channels are adaptive to the characteristics of signals and the background, and they capture the interactions among signal locations. Corresponding linear decision templates are employed to generate both image-level and location-specific scores on the presence of signals. Our results show that: (1) the model observer can achieve high performance with a reasonably small number of channels; (2) the model observer with PLS channels outperforms that with benchmark modified Laguerre–Gauss channels, especially when realistic signal shapes and complex background statistics are involved; (3) the tasks of clinical interest, and other constraints such as sample size would alter the optimal design of the model observer.

Image Quality Assessment Based on Local Linear Information and Distortion-Specific Compensation.

Image quality assessment (IQA) is a fundamental yet constantly developing task for computer vision and image processing. Most IQA evaluation mechanisms are based on the pertinence of subjective and objective estimation. Each image distortion type has its own property correlated with human perception. However, this intrinsic property may not be fully exploited by existing IQA methods. In this paper, we make two main contributions to the IQA field. First, a novel IQA method is developed based on a local linear model that examines the distortion between the reference and the distorted images for better alignment with human visual experience. Second, a distortion-specific compensation strategy is proposed to offset the negative effect on IQA modeling caused by different image distortion types. These score offsets are learned from several known distortion types. Furthermore, for an image with an unknown distortion type, a convolutional neural network-based method is proposed to compute the score offset automatically. Finally, an integrated IQA metric is proposed by combining the aforementioned two ideas. Extensive experiments are performed to verify the proposed IQA metric, which demonstrate that the local linear model is useful in human perception modeling, especially for individual image distortion, and the overall IQA method outperforms several state-of-the-art IQA approaches.

A CNN based neurobiology inspired approach for retinal image quality assessment.

Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach to feature learning and hence higher accuracy in decision making. Experimental results demonstrate the superior performance of our proposed algorithm over competing methods.

Full-Reference Image Quality Assessment with Linear Combination of Genetically Selected Quality Measures.

Information carried by an image can be distorted due to different image processing steps introduced by different electronic means of storage and communication. Therefore, development of algorithms which can automatically assess a quality of the image in a way that is consistent with human evaluation is important. In this paper, an approach to image quality assessment (IQA) is proposed in which the quality of a given image is evaluated jointly by several IQA approaches. At first, in order to obtain such joint models, an optimisation problem of IQA measures aggregation is defined, where a weighted sum of their outputs, i.e., objective scores, is used as the aggregation operator. Then, the weight of each measure is considered as a decision variable in a problem of minimisation of root mean square error between obtained objective scores and subjective scores. Subjective scores reflect ground-truth and involve evaluation of images by human observers. The optimisation problem is solved using a genetic algorithm, which also selects suitable measures used in aggregation. Obtained multimeasures are evaluated on four largest widely used image benchmarks and compared against state-of-the-art full-reference IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing measures.

Objective Image Quality Assessment Based on Saliency Map

The development of objective image quality assessment metrics aligned with human perception is of fundamental importance to numerous image processing applications. In this paper, an objective image quality assessment approach based on saliency map is proposed. By local shift estimation method, the retargeted image is resized to the same size as the reference image. A gradient magnitude similarity map is computed by comparing the retargeted and reference images. The more similarly, the brighter of pixels in the gradient magnitude similarity map. At the same time, a saliency map of reference image is achieved by visual attention. Finally, an overall image quality score is computed from the gradient magnitude similarity map via saliency pooling strategy. The most important step in our approach is to generate a gradient magnitude similarity map that indicates at each spatial location in the source image how the structural information is preserved in the retargeted image. There are two key contributions in this paper, one is that we add the texture feature in computing saliency map because image gradient is very sensitive to texture information, and the other is that we propose a new objective image quality metrics by introducing saliency map into image quality evaluation. Experimental results indicate that the evaluation indexes of our approach are better than existing methods in the literature.

Decision Fusion for Image Quality Assessment using an Optimization Approach

The proliferation of electronic means of communication entails distortion of visual information carried by processed images. Therefore, automatic evaluation of image perceptual quality in a way that is consistent with human perception is important. In this letter, an approach to full-reference image quality assessment (IQA) is proposed. The perceptual quality of the image is evaluated using an aggregated decision of several IQA measures. An optimization problem of designing a decision fusion of 18 IQA measures is defined and solved using a genetic algorithm. Obtained fusion strategies are evaluated on widely used large image benchmarks and compared with 32 state-of-the-art IQA approaches. Results of comparison reveal that the proposed approach outperforms other competing techniques.

Blind image quality assessment by relative gradient statistics and adaboosting neural network

The image gradient is a commonly computed image feature and a potentially predictive factor for image quality assessment (IQA). Indeed, it has been successfully used for both full- and no- reference image quality prediction. However, the gradient orientation has not been deeply explored as a predictive source of information for image quality assessment. Here we seek to amend this by studying the quality relevance of the relative gradient orientation, viz., the gradient orientation relative to the surround. We also deploy a relative gradient magnitude feature which accounts for perceptual masking and utilize an AdaBoosting back-propagation (BP) neural network to map the image features to image quality. The generalization of the AdaBoosting BP neural network results in an effective and robust quality prediction model. The new model, called Oriented Gradients Image Quality Assessment (OG-IQA), is shown to deliver highly competitive image quality prediction performance as compared with the most popular IQA approaches. Furthermore, we show that OG-IQA has good database independence properties and a low complexity.

Content-weighted mean-squared error for quality assessment of compressed images

Image quality assessment (IQA) has been intensively studied, especially for the full-reference (FR) scenario. However, only the mean-squared error (MSE) is widely employed in compression. Why other IQA metrics work ineffectively? We first sum up three main limitations including the computational time, portability, and working manner. To address these problems, we then in this paper propose a new content-weighted MSE (CW-MSE) method to assess the quality of compressed images. The design principle of our model is to use adaptive Gaussian convolution to estimate the influence of image content in a block-based manner, thereby to approximate the human visual perception to image quality. Results of experiments on six popular subjective image quality databases (including LIVE, TID2008, CSIQ, IVC, Toyama and TID2013) confirm the superiority of our CW-MSE over state-of-the-art FR IQA approaches.

Biologically inspired image quality assessment

Image quality assessment (IQA) aims at developing computational models that can precisely and automatically estimate human perceived image quality. To date, various IQA methods have been proposed to mimic the processing of the human visual system, with limited success. Here, we present a novel IQA approach named biologically inspired feature similarity (BIFS), which is demonstrated to be highly consistent with the human perception. In the proposed approach, biologically inspired features (BIFs) of the test image and the relevant reference image are first extracted. Afterwards, local similarities between the reference BIFs and the distorted ones are calculated and then combined to obtain a final quality index. Thorough experiments on a number of IQA databases demonstrate that the proposed method is highly effective and robust, and outperform state-of-the-art FR-IQA methods across various datasets.

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

Application specific information processing (ASIP) unit in smart cameras requires sophisticated image processing algorithms for image quality improvement and extraction of relevant features for image understanding and machine vision. The improvement in performance as well as robustness can be achieved by intelligent moderation of the parameters both at algorithm (image resolution, contrast, compression, and so on) as well as hardware levels (camera orientation, field of view, and so on). This paper discusses the employment of ISO/IEC/IEEE 21451 smart transducer standards for performance improvement of smart cameras. The standardized transducer electronic data sheets (TEDS-by IEEE 21450) provide the self description of sensors, of which the calibration details are of vital importance to yield a smart and reconfigurable imaging system. This is possible by exercising intelligent control over the TEDS (smart camera) calibration details as well as automated tuning of algorithm parameters (in ASIP) based on decisions by perceptually efficient image quality assessment (IQA) tool. Estimation of distortion based on reduced reference IQA has been highlighted as a reliable methodology for this purpose. The proposed IQA approach uses wavelets for features extraction followed by estimation of luminance, contrast, and divergence parameters to obtain the proposed distortion measure (Q). The computational complexity in the process has been catalyzed using integral image and gradient magnitude approaches. The validation of Q metric is carried out by evaluating the image quality for various types of distortions on images from Content-based Strategies of Image Quality assessment (CSIQ) and Information Visualization CyberInfrastructure (IVC) databases. Simulation results yield a healthy correlation of Q and the subjective human opinions.

Image similarity metric (ISIM): a reduced reference image quality assessment approach

Reduced reference image quality assessment (IQA) has been highlighted as a reliable methodology for the purpose of quality-of-service (QoS) monitoring in network visual communication applications; as it requires only selective features of the reference image where complete image cannot be accessed (as reference for IQA). The reduced reference IQA problem has been constrained towards extraction of appropriate features (of reference and transformed images) sensitive to various distortion types along with due coherence with human perception. This paper presents an improved reduced reference IQA approach named as ‘ISIM: Image Similarity Metric’ based on natural image statistics. The proposed IQA approach involves sub-band decomposition of acquired images for features extraction using wavelets. The extracted features are then used to obtain a global distortion measure (Q) as a function of luminance, contrast and divergence parameters. The computational complexity during features extraction is reduced by employing combination of integral image and gradient magnitude approaches. This distortion measure (Q) is then used to formulate ISIM metric as a linear regression with full-reference SSIM. The validation of the proposed metric is carried out by evaluating the image quality for various types of distortions from CSIQ and IVC databases. Simulation results yield a healthy correlation of proposed ISIM metric and the subjective human opinions along with minimal computational complexity in comparison to other state-of-art reduced reference IQA measures.

A structure fidelity approach for big data collection in wireless sensor networks.

One of the most widespread and important applications in wireless sensor networks (WSNs) is the continuous data collection, such as monitoring the variety of ambient temperature and humidity. Due to the sensor nodes with a limited energy supply, the reduction of energy consumed in the continuous observation of physical phenomenon plays a significant role in extending the lifetime of WSNs. However, the high redundancy of sensing data leads to great waste of energy as a result of over-deployed sensor nodes. In this paper, we develop a structure fidelity data collection (SFDC) framework leveraging the spatial correlations between nodes to reduce the number of the active sensor nodes while maintaining the low structural distortion of the collected data. A structural distortion based on the image quality assessment approach is used to perform the nodes work/sleep scheduling, such that the number of the working nodes is reduced while the remainder of nodes can be put into the low-power sleep mode during the sampling period. The main contribution of SFDC is to provide a unique perspective on how to maintain the data fidelity in term of structural similarity in the continuous sensing applications for WSNs. The simulation results based on synthetic and real world datasets verify the effectiveness of SFDC framework both on energy saving and data fidelity.

Hybrid No-Reference Quality Metric for Singly and Multiply Distorted Images

In a typical image communication system, the visual signal presented to the end users may undergo the steps of acquisition, compression and transmission which cause the artifacts of blurring, quantization and noise. However, the researches of image quality assessment (IQA) with multiple distortion types are very limited. In this paper, we first introduce a new multiply distorted image database (MDID2013), which is composed of 324 images that are simultaneously corrupted by blurring, JPEG compression and noise injection. We then propose a new six-step blind metric (SISBLIM) for quality assessment of both singly and multiply distorted images. Inspired by the early human visual model and recently revealed free energy based brain theory, our method works to systematically combine the single quality prediction of each emerging distortion type and joint effects of different distortion sources. Comparative studies of the proposed SISBLIM with popular full-reference IQA approaches and start-of-the-art no-reference IQA metrics are conducted on five singly distorted image databases (LIVE, TID2008, CSIQ, IVC, Toyama) and two newly released multiply distorted image databases (LIVEMD, MDID2013). Experimental results confirm the effectiveness of our blind technique. MATLAB codes of the proposed SISBLIM algorithm and MDID2013 database will be available online at http://gvsp.sjtu.edu.cn/.

A no-reference image quality assessment approach based on steerable pyramid decomposition using natural scene statistics

For majorities of no-reference image quality assessment (NRIQA) algorithms, prior knowledge about image distortion types is needed. However, the source distortions in the image are actually not giv...

How to assess image quality within a workflow chain: an overview

Image quality assessment (IQA) is a multi-dimensional research problem and an active and evolving research area. This paper aims to provide an overview of the state of the art of the IQA methods, putting in evidence their applicability and limitations in different application domains. We outline the relationship between the image workflow chain and the IQA approaches reviewing the literature on IQA methods, classifying and summarizing the available metrics. We present general guidelines for three workflow chains in which IQA policies are required. The three workflow chains refer to: high-quality image archives, biometric system and consumer collections of personal photos. Finally, we illustrate a real case study referring to a printing workflow chain, where we suggest and actually evaluate the performance of a set of specific IQA methods.

An Image Quality Assessment Approach Based on Saliency Map in Space Domain

This paper proposes a no-reference image assessment approach (IQA) based on saliency map in the space domain of the image. The saliency map of the image is extracted by Itti model at first. Next, the saliency-map weighted normalized image is used to get the histogram of the image, then the histogram is modeled by generalized gaussian distribution and the parameter of the generalized gaussian distribution is estimated by parameter estimating approach. Parameters of the generalized gaussian distribution are used as the feature vector for the training and testing image. The feature vectors of the testing image are fed to support vector regresion machine to evaluate the image quality score. Experimental results show that our approach outperforms the recent method of no-reference IQA.

Perceptual image quality assessment metric using mutual information of Gabor features

A good objective metric of image quality assessment (IQA) should be consistent with the subjective judgment of human beings. In this paper, a four-stage perceptual approach for full reference IQA is presented. In the first stage, the visual features are extracted by 2-D Gabor filter that has the excellent performance of modeling the receptive fields of simple cells in the primary visual cortex. Then in the second stage, the extracted features are post-processed by the divisive normalization transform to reflect the nonlinear mechanisms in human visual systems. In the third stage, mutual information between the visual features of the reference and distorted images is employed to measure the visual quality. And in the last pooling stage, the mutual information is converted to the final objective quality score. Experimental results show that the proposed metic has a high correlation with the subjective assessment and outperforms other state-of-the-art metrics.

基于生物视觉标准模型特征的无参考型图像质量评价方法

基于生物视觉标准模型特征的无参考型图像质量评价方法

Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems. DOI: http://dx.doi.org/10.11591/ijece.v3i6.5034

Channelized model observer for the detection and estimation of signals with unknown amplitude, orientation, and size

As a task-based approach for medical image quality assessment, model observers (MOs) have been proposed as surrogates for human observers. While most MOs treat only signal-known-exactly tasks, there are few studies on signal-known-statistically (SKS) MOs, which are clinically more relevant. In this paper, we present a new SKS MO named channelized joint detection and estimation observer (CJO), capable of detecting and estimating signals with unknown amplitude, orientation, and size. We evaluate its estimation and detection performance using both synthesized (correlated Gaussian) backgrounds and real clinical (magnetic resonance) backgrounds. The results suggest that the CJO has good performance in the SKS detection-estimation task.