- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis
摘要： Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a purpose, it is annoying. There exist significant statistical differences between distortion-free natural images and noisy images that become evident upon comparing the empirical probability distribution histograms of their discrete wavelet transform (DWT) coefficients. The DWT coefficients of low- or no-noise natural images have leptokurtic, peaky distributions with heavy tails; while noisy images tend to be platykurtic with less peaky distributions and shallower tails. The sample kurtosis is a natural measure of the peakedness and tail weight of the distributions of random variables. Here, we study the efficacy of the sample kurtosis of image wavelet coefficients as a feature driving an extreme learning machine which learns to map kurtosis values into perceptual quality scores. The model is trained and tested on five types of noisy images, including additive white Gaussian noise, additive Gaussian color noise, impulse noise, masked noise, and high-frequency noise from the LIVE, CSIQ, TID2008, and TID2013 image quality databases. The experimental results show that the trained model has better quality evaluation performance on noisy images than existing blind noise assessment models, while also outperforming general-purpose blind and full-reference image quality assessment methods.
关键词： sub-band,discrete wavelet transform (DWT),extreme learning machine (ELM),kurtosis,Blind noisy image quality assessment
更新于2025-09-23 15:23:52
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Blind Stereoscopic Image Quality Assessment Based on Hierarchical Learning
摘要： We proposed a blind image quality assessment model which used classification and prediction for three-dimensional (3D) image quality assessment (denoted as CAP-3DIQA) that can automatically evaluate the quality of stereoscopic images. First, in the classification stage, the model separated the distorted images into several subsets according to the types of image distortions. This process will assign the images with the same distortion type to the same group. After the classification stage, the classified distorted image set is fed into the image quality predictor that contains five different perceptual channels which predict the image quality score individually. Lastly, we used the regression module of support vector machine to evaluate the final image quality score where the input of the regression model is the combination of five channel's outputs. The model we proposed is tested on three public and popular databases, which are LIVE 3D Image Quality Database Phase I, LIVE 3D Image Quality Database Phase II and MCL 3D Image Quality Database. The experimental results show that our proposed model leads to significant performance improvement on quality prediction for stereoscopic images compared with other existing state-of-the-art quality metrics.
关键词： image quality assessment,stereoscopic images,Hierarchical learning,no reference
更新于2025-09-23 15:23:52
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A multi-order derivative feature-based quality assessment model for light field image
摘要： This paper presents an image quality assessment (IQA) model exploring the multi-order derivative feature, called Multi-order Derivative Feature-based Model (MDFM), for evaluating the perceptual quality of light field image (LFI). In our approach, for the input reference and distorted LFIs, the multi-order derivative features are extracted by using the discrete derivative filter to represent the image details in different degrees. Then, the similarities of the extracted derivative features are measured independently. Finally, the weight map is established through the maximum value of the second-order derivative feature of reference and distorted LFIs, which is further utilized to pool the similarity map for generating the final score. Extensive simulation results have demonstrated that the proposed MDFM is more consistent with the perception of the HVS on the evaluation of LFI than the classical and state-of-the-art IQA methods.
关键词： Multi-order derivative feature,Light field image,Image quality assessment
更新于2025-09-23 15:23:52
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Blind image quality assessment with hierarchy: Degradation from local structure to deep semantics
摘要： Though blind image quality assessment (BIQA) is highly desired in perceptual-oriented image processing systems, it is extremely difficult to design a reliable BIQA method. With the help of the prior knowledge, the human visual system (HVS) hierarchically perceives the quality degradation during the visual recognition. Inspired by this, we suggest different levels of distortion generate individual degradations on hierarchical features, and propose to consider the degradations on both low and high level features for quality prediction. By mimicking the orientation selectivity (OS) mechanism in the primary visual cortex, an OS based local structure is designed for low-level visual information representation. At the meantime, the deep residual network, which possesses multiple levels for feature integration, is employed to extract the deep semantics for high-level visual content representation. By fusing the local structure and the deep semantics, a hierarchical feature set is acquired. Next, the correlations between the degradations of image qualities and their corresponding hierarchical feature sets are analyzed, and a novel hierarchical feature degradation (HFD) based BIQA (HFD-BIQA) method is built. Experimental results on the legacy and wild image quality assessment databases demonstrate the prediction accuracy of the proposed HFD-BIQA method, and verify that the HFD-BIQA performs highly consistent with the subjective perception.
关键词： Local structure,Deep semantics,Hierarchical feature degradation,Blind image quality assessment
更新于2025-09-23 15:23:52
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Blind Quality Metric for Contrast-Distorted Images Based on Eigendecomposition of Color Histograms
摘要： Although contrast is a major issue in overall quality assessment of an image, existing contrast evaluators with a reasonable performance are currently scarce. Here, we propose a learning-based blind/no-reference (NR) image quality assessment (IQA) model, dubbed Histogram Eigen-Feature based Contrast Score (HEFCS) for evaluating image contrast. This research seeks for the inter-relationship between contrast degradation and relevant image histogram features. We introduce "eigen-histograms", which are the eigenvectors of the set of image patches' histograms. We found that the randomness of image eigen-histograms and the amplitude of corresponding eigenvalues can reliably reflect the changes in image contrast. Employing these characteristics leads to contrast-aware Histogram Eigen-Feature (HEF) vectors, which are used to compute the contrast score through a prediction model trained using support vector regression (SVR). Extensive analysis and cross validation are performed with five contrast relevant image databases, and the HEFCS performance results are compared with a collection of full-reference (FR), reduced-reference (RR) and no-reference measures. Despite its simplicity and low computational complexity, the HEFCS performs better than all competing NR-IQA models, and also stands among the three best-performers of FR and RR models.
关键词： no-reference/blind,image quality assessment (IQA),Contrast distortion,eigen-histogram
更新于2025-09-23 15:23:52
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Quality assessment and damage detection in nanomodified adhesively-bonded composite joints using inkjet-printed interdigital sensors
摘要： In this work, the development of a planar interdigital capacitive sensor, directly onto the surface of a composite, for determining the initial quality of curing of bonded composite joints and assessing their long-term durability is presented. The sensor consisted of an interlocking comb-shaped array of silver electrodes and used to monitor the progress of cure of an adhesive resin and the subsequent damage state of the bond line in adhesively-bonded composite joints using impedance spectroscopy. The obtained results from the mechanical characterization indicated that the developed sensor did not affect the quality of the bondline while the added weight of the sensor is negligible. The curing process of the adhesive epoxy was successfully monitored while the ability of the sensor to assess the developed damage created by the mechanical loading was confirmed using transient infrared thermography.
关键词： quality assessment,inkjet printing,Bonded composite structures,damage detection,interdigital sensors
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - Shenzhen, China (2018.7.13-2018.7.15)] 2018 IEEE 3rd International Conference on Signal and Image Processing (ICSIP) - A Lightweight Quality Assessment of Screen Content Images using Directional Derivative Filters
摘要： In this paper, we present a lightweight visual quality assessment of screen content image (SCI) based on the local luminance edge directions and gradient magnitude. First, we use directional derivative filters (DDFs) to extract the edge direction feature which is one of the main characteristics of SCIs. To obtain the perceptual quality measures, we separately extract the edge direction and gradient magnitude for the similarity computation between the reference and distorted SCIs. Finally, considering the computational complexity, we incorporate the DDF-based feature map with the gradient magnitude map together to generate a new visual quality metric. Experimental results have demonstrated that the proposed method is able to adapt better to the human visual system than 12 representative methods based on the screen image quality assessment database (SIQAD).
关键词： screen content images,directional derivatives,Image quality assessment (IQA),human visual system
更新于2025-09-23 15:22:29
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A Novel Patch Variance Biased Convolution Neural Network for No-Reference Image Quality Assessment
摘要： Deep Convolutional Neural Networks (CNNs) have been successfully applied on no-reference image quality assessment (NR-IQA) with respect to human perception. Most of these methods deal with small image patches and use the average score of the test patches for predicting the whole image quality. We discovered that image patches from homogenous regions are unreliable for both neural network training and final image quality score estimation. In addition, image patches with complex structures have much higher chances to achieve better image quality prediction. Based on these findings, we enhanced the conventional CNN-based NR-IQA algorithm to avoid homogenous patches for the network training and quality score estimation. Moreover, we also use a variance-based weighting average to bias the final image quality score to the patches with complex structure. Experimental results show that this simple approach can achieve state-of-the-art performance as compared with well-known NR-IQA algorithms.
关键词： deep learning,no-reference image quality assessment,convolution neural network
更新于2025-09-23 15:22:29
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PET Counting Response Variability Depending on Tumor Location, Activity, and Patient Obesity: A Feasibility Study of Solitary Pulmonary Nodule Using Monte Carlo
摘要： We aim to investigate the counting response variations of Positron Emission Tomography (PET) scanners with different detector configurations in the presence of Solitary Pulmonary Nodule (SPN). Using experimentally validated Monte Carlo simulations, the counting performance of four different scanner models with varying tumor activity, location, and patient obesity is represented using NECR (Noise Equivalent Count Rate). NECR is a well-established quantitative metric which has positive correlation with clinically perceived image quality. The combined effect of tumor displacement and increased activity shows a linear ascending trend for NECR with slope ranges of (12.5–18.2)*10-3 (kBq/cm3)-1 for three-ring (3R) scanners and (15.3–21.5)*10-3 (kBq/cm3)-1 for four-ring (4R). The trend for the combined effect of tumor displacement and patient obesity is exponential decay with 3R configurations weakly dependent on the patient obesity if the tumor is located at the center of the field-of-view with exponent’s range of (6.6-33.8)*10-2 cm-1. The dependency is stronger for 4R scanners (9.6–38.5)*10 -2 cm-1. The analysis indicates that quantitative PET data from the same SPN patient possibly examined in different time points (e.g. during staging or for the evaluation of treatment response) are affected by the different detector configurations and need to be normalized with patient weight, activity, and tumor location to reduce unwanted bias of the diagnosis. Our work provides also with a proof of concept for the ability of properly tuned simulations to provide additional insights into the counting response variability especially in tumor types where often borderline decisions have to be made regarding their characterization.
关键词： image quality assessment,nuclear imaging,lung
更新于2025-09-23 15:22:29
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Local Feature Descriptor and Derivative Filters for Blind Image Quality Assessment
摘要： In this letter, a novel Blind Image Quality Assessment (BIQA) technique is introduced to provide an automatic and reproducible evaluation of distorted images. In the approach, the information carried by image derivatives of different orders is captured by local features and used for the image quality prediction. Since a typical local feature descriptor is designed to ensure a robust image patch representation, in this letter, a novel descriptor which additionally highlights local differences enhanced by the filtering is proposed. Furthermore, a set of derivative kernels is introduced. Finally, the support vector regression (SVR) technique is used to map statistics of described features into subjective scores, providing an objective quality score for an image. Extensive experimental validation on popular IQA image datasets reveals that the proposed method outperforms the state-of-the-art hand-crafted and deep learning BIQA measures.
关键词： Local features,Feature descriptor,Support vector regression,Derivative filters,Blind image quality assessment
更新于2025-09-23 15:22:29