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- 摘要
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- 实验方案
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Automatic Assessment of Full Left Ventricular Coverage in Cardiac Cine Magnetic Resonance Imaging with Fisher Discriminative 3D CNN
摘要： Cardiac magnetic resonance (CMR) images play a growing role in the diagnostic imaging of cardiovascular diseases. Full coverage of the left ventricle (LV), from base to apex, is a basic criterion for CMR image quality and necessary for accurate measurement of cardiac volume and functional assessment. Incomplete coverage of the LV is identified through visual inspection, which is time-consuming and usually done retrospectively in the assessment of large imaging cohorts. This paper proposes a novel automatic method for determining LV coverage from CMR images by using Fisher-discriminative three-dimensional (FD3D) convolutional neural networks (CNNs). In contrast to our previous method employing 2D CNNs, this approach utilizes spatial contextual information in CMR volumes, extracts more representative high-level features and enhances the discriminative capacity of the baseline 2D CNN learning framework, thus achieving superior detection accuracy. A two-stage framework is proposed to identify missing basal and apical slices in measurements of CMR volume. First, the FD3D CNN extracts high-level features from the CMR stacks. These image representations are then used to detect the missing basal and apical slices. Compared to the traditional 3D CNN strategy, the proposed FD3D CNN minimizes within-class scatter and maximizes between-class scatter. We performed extensive experiments to validate the proposed method on more than 5,000 independent volumetric CMR scans from the UK Biobank study, achieving low error rates for missing basal/apical slice detection (4.9%/4.6%). The proposed method can also be adopted for assessing LV coverage for other types of CMR image data.
关键词： image-quality assessment,LV coverage,Fisher discriminant criterion,3D convolutional neural network,population image analysis
更新于2025-09-09 09:28:46
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A Reduced-Reference Image Quality Assessment Model Based on Joint-Distribution of Neighboring LOG Signals
摘要： Previous work have validated that the output of retinal ganglion cells in human visual pathway, which can be modeled as an LOG (Laplacian of Gaussian) filtration, can whiten the power spectrum of not only the natural images, but also the distorted images, hence the first-order (average luminance) and the second-order (contrast) redundancies have been removed when applying the LOG filtration. Considering the fact that human vision system (HVS) always ignores the first-order and the second-order information when sensing image local structures, the LOG signals should be efficient features in IQA (image quality assessment) task and a lot of LOG based IQA models have been proposed. In this paper, we focus on an interesting question that has not been investigated carefully yet: what is an efficient way to represent image structure features that is perceptual quality aware based on relationship between the LOG signals. We examine the to represent neighboring LOG signals and propose relationship by computing the joint distribution of neighboring LOG signals, and thus propose a set of simple but efficient RR IQA feature and consequently yield an excellent RR IQA model. Experimental results on three large scale subjective IQA databases show that our proposed method works robustly across different databases and stay in the state-of-the-art RR IQA models.
关键词： Whiten,Reduced-Reference,Laplacian of Gaussian,Image Quality Assessment,Joint Distribution
更新于2025-09-09 09:28:46
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Blind Image Quality Assessment Using Multiscale Local Binary Patterns
摘要： This article proposes a new no-reference image quality assessment method that is able to blindly predict the quality of an image. The method is based on a machine learning technique that uses texture descriptors. In the proposed method, texture features are computed by decomposing images into texture information using multiscale local binary pattern (MLBP) operators. In particular, the parameters of local binary pattern operators are varied, which generates MLBP operators. The features used for training the prediction algorithm are the histograms of these MLBP channels. The results show that, when compared with other state-of-the-art no-reference methods, the proposed method is competitive in terms of prediction precision and computational complexity.
关键词： MLBP,machine learning,multiscale local binary pattern,texture descriptors,no-reference image quality assessment
更新于2025-09-09 09:28:46
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Visual-quality Guided Global Backlight Dimming for Video Display on Mobile Devices
摘要： This paper proposes a visual-quality guided global backlight dimming (VQG-GBD) algorithm to reduce the power consumption of liquid-crystal display (LCD) on mobile devices. We build a backlight scaling ratio (BSR) prediction model via visual quality assessment (VQA) which considers not only display contents but also backlight intensity while measuring video quality. Also, we add visual uncertainty as an indicator to dim the backlight without being noticed by observers. VQG-GBD includes a training stage and online stage. For training stage, firstly, we collect videos with distinct attributes of brightness and uncertainty. Then, subjective rating obtains the relationship among visual quality, BSR, brightness, and visual uncertainty. Finally, we use the trust-region method (TRM) to build the BSR prediction model. In online stage, the model is applied to mobile devices for real-time video display. And a BSR optimization strategy is proposed to eliminate the flicker effect between frames, followed by three techniques to accelerate the process: motion vector (MV) extraction and pixel subsampling reduce the computation while analyzing frame content; GPU rendering speed up the pixel compensation. Experimental results show that VQG-GBD achieves averagely 21% of power demand reduction for displaying video on mobile devices while preserving good visual quality. VQG-GBD delivers more power reduction than the state-of-the-art algorithm image integrity-based gray-level error control (I2GEC) and multi-histogram-based gray-level error control (MGEC) by 10% and 8% respectively.
关键词： Global backlight dimming,mobile devices,visual uncertainty,ACR11,subjective visual quality assessment,liquid-crystal display,trust-region method
更新于2025-09-09 09:28:46
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Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
摘要： We propose a deep bilinear model for blind image quality assessment (BIQA) that works for both synthetically and authentically distorted images. Our model constitutes two streams of deep convolutional neural networks (CNN), specializing in the two distortion scenarios separately. For synthetic distortions, we first pre-train a CNN to classify the distortion type and level of an input image, whose ground truth label is readily available at a large scale. For authentic distortions, we make use of a pre-train CNN (VGG-16) for the image classification task. The two feature sets are bilinearly pooled into one representation for a final quality prediction. We fine-tune the whole network on target databases using a variant of stochastic gradient descent. Extensive experimental results show that the proposed model achieves state-of-the-art performance on both synthetic and authentic IQA databases. Furthermore, we verify the generalizability of our method on the large-scale Waterloo Exploration Database, and demonstrate its competitiveness using the group maximum differentiation competition methodology.
关键词： Blind image quality assessment,convolutional neural networks,bilinear pooling,perceptual image processing,gMAD competition
更新于2025-09-09 09:28:46
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Feature selection algorithm for no-reference image quality assessment using natural scene statistics
摘要： Images play an essential part in our daily lives and the performance of various imaging applications is dependent on the user’s quality of experience. No-reference image quality assessment (NR-IQA) has gained importance to assess the perceived quality, without using any prior information of the nondistorted version of the image. Different NR-IQA techniques that utilize natural scene statistics classify the distortion type based on groups of features and then these features are used for estimating the image quality score. However, every type of distortion has a different impact on certain sets of features. In this paper, a new feature selection algorithm is proposed for distortion identification based image verity and integration evaluation that selects distinct feature groups for each distortion type. The selection procedure is based on the contribution of each feature on the Spearman rank order correlation constant (SROCC) score. Only those feature groups are used in the prediction model that have majority features with SROCC score greater than mean SROCC score of all the features. The proposed feature selection algorithm for NR-IQA shows better performance in comparison to state-of-the-art NR-IQA techniques and other feature selection algorithms when evaluated on three commonly used databases.
关键词： feature selection,No-reference image quality assessment,classification,distortion identification based image verity and integration evaluation,support vector regression
更新于2025-09-04 15:30:14
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Feature comparison and analysis for new challenging research fields of image quality assessment
摘要： Nowadays, objective image quality assessment (IQA) issue becomes increasingly important for both the practical application and scientific research in digital image processing systems. In this paper, we conduct feature comparison and analysis on new challenging research fields including contrast-distorted, screen content, multiply-distorted, tone-mapped, Depth Image Based Rendering (DIBR) and authentically distorted IQA. We describe the performance criteria, design rationale, and benchmark databases for validating IQA metrics in accordance with human perceptions. Then we provide some important conclusions of feature selection in terms of different IQA problems. In this work, our goal is dominantly towards judging the robustness of mainstream features used in the IQA field, which can help current and emerging researchers for better and faster grasping the speciality of each of novel IQA directions reported.
关键词： Image quality assessment (IQA),DIBR distortion,Authentical distortion,Multiple distortion
更新于2025-09-04 15:30:14
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Blind quality assessment for screen content images by combining local and global features
摘要： Recently, several no-reference image quality assessment (NR-IQA) metrics have been developed for the quality evaluation of screen content images (SCIs). While, most of them are opinion-aware methods, which are limited by the subjective opinion scores of training data. Hence, in this paper, we propose a novel opinion-unaware method to predict the quality of SCIs without any prior information. Firstly, an union feature is proposed by considering the local and global visual characteristics of human visual system simultaneously. Specifically, a local structural feature is extracted from the rough and smooth regions of SCIs by leveraging a sparse representation model. As a supplement, a global feature is obtained by combining the luminance statistical feature and local binary pattern (LBP) feature of entire SCIs. Secondly, to get rid of the limitation of subjective opinion scores, a new large-scale training dataset contained 80,000 distorted SCIs is constructed, and the quality labels of those distorted SCIs are derived by an advanced full-reference IQA metric. Thirdly, a regression model between image features and image quality labels is learned from the training dataset by employing a learning-based framework. And then, the quality scores of test SCIs can be predicted by the pre-trained regression model. The experimental results on two largest SCI-oriented databases show that the proposed method is superior to the state-of-the-art NR-IQA metrics.
关键词： Local binary patterns,Image quality assessment,Sparse representation,No-reference,Screen content image
更新于2025-09-04 15:30:14
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Roma, Italy (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - A Study on the Impact of Visualization Techniques on Light Field Perception
摘要： Light Field imaging is a promising technology that allows to capture the whole set of light rays in a scene thus enabling the generation of perspective views from any position. This possibility can be exploited in several application scenarios, such as virtual and augmented reality or depth estimation. In this framework many issues arise due different aspects such as the large amount of generated data or to the need of dedicated and expensive hardware for Light Field capturing. Moreover, the Light Field carries information about the entire scene and the data that is delivered to the users largely differs from the traditional 2D and 3D media in terms of content and way of fruition. Dedicated rendering technology and devices for the Light Field are nowadays still not mature or quite expensive and the best option is to render the Light Field data on a conventional 2D screen. Consequently, there is the need for finding the best visualization technique that allows to exploit the information in the Light Field while being accepted by the viewers. In this paper we address this issue by considering six visualization options and by running experimental tests to study which is the technique preferred by the users.
关键词： Light Field,subjective quality assessment,2D rendering,visualization techniques
更新于2025-09-04 15:30:14
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Roma, Italy (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Performance Evaluation of N O- Reference Image Quality Metrics for Visible Wavelength Iris Biometric Images
摘要： Image quality assessment plays an important role in iris recognition systems because the system performance is affected by low quality iris images. With the development of electronic color imaging, there are more and more researches about visible wavelength (VW) iris recognition. Compared to the near infrared iris images, using VW iris images acquired under unconstrained imaging conditions is a more challenging task for the iris recognition system. However, the number of quality assessment methods for VW iris images is limited. Therefore, it is interested to investigate whether existing no-reference image quality metrics (IQMs) which are designed for natural images can assess the quality of VW iris images. In this paper, we evaluate the performance of 15 selected no-reference IQMs on VW iris biometrics. The experimental results show that several IQMs can assess iris sample quality according to the system performance.
关键词： image quality assessment,visible wavelength iris,image based attributes,performance evaluation,multi-modality
更新于2025-09-04 15:30:14