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Journal articles on the topic 'No-Reference image quality assessment (NR-IQA)'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

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1

Zhang, Haopeng, Bo Yuan, Bo Dong, and Zhiguo Jiang. "No-Reference Blurred Image Quality Assessment by Structural Similarity Index." Applied Sciences 8, no. 10 (2018): 2003. http://dx.doi.org/10.3390/app8102003.

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No-reference (NR) image quality assessment (IQA) objectively measures the image quality consistently with subjective evaluations by using only the distorted image. In this paper, we focus on the problem of NR IQA for blurred images and propose a new no-reference structural similarity (NSSIM) metric based on re-blur theory and structural similarity index (SSIM). We extract blurriness features and define image blurriness by grayscale distribution. NSSIM scores an image quality by calculating image luminance, contrast, structure and blurriness. The proposed NSSIM metric can evaluate image quality

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Shi, Jinsong, Pan Gao, and Jie Qin. "Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 5 (2024): 4829–37. http://dx.doi.org/10.1609/aaai.v38i5.28285.

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Image Quality Assessment (IQA) has long been a research hotspot in the field of image processing, especially No-Reference Image Quality Assessment (NR-IQA). Due to the powerful feature extraction ability, existing Convolution Neural Network (CNN) and Transformers based NR-IQA methods have achieved considerable progress. However, they still exhibit limited capability when facing unknown authentic distortion datasets. To further improve NR-IQA performance, in this paper, a novel supervised contrastive learning (SCL) and Transformer-based NR-IQA model SaTQA is proposed. We first train a model on

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Lee, Wonkyeong, Eunbyeol Cho, Wonjin Kim, et al. "No-reference perceptual CT image quality assessment based on a self-supervised learning framework." Machine Learning: Science and Technology 3, no. 4 (2022): 045033. http://dx.doi.org/10.1088/2632-2153/aca87d.

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Abstract Accurate image quality assessment (IQA) is crucial to optimize computed tomography (CT) image protocols while keeping the radiation dose as low as reasonably achievable. In the medical domain, IQA is based on how well an image provides a useful and efficient presentation necessary for physicians to make a diagnosis. Moreover, IQA results should be consistent with radiologists’ opinions on image quality, which is accepted as the gold standard for medical IQA. As such, the goals of medical IQA are greatly different from those of natural IQA. In addition, the lack of pristine reference i

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Ismail, Taha Ahmed, Soong Der Chen, Tareq Hammad Baraa, and Jamil Norziana. "Contrast-distorted image quality assessment based on curvelet domain features." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2595–603. https://doi.org/10.11591/ijece.v11i3.pp2595-2603.

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Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the Pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain fe

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Oszust, Mariusz. "No-Reference Image Quality Assessment with Local Gradient Orientations." Symmetry 11, no. 1 (2019): 95. http://dx.doi.org/10.3390/sym11010095.

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Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are process

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Ahmed, Ismail Taha, Chen Soong Der, Baraa Tareq Hammad, and Norziana Jamil. "Contrast-distorted image quality assessment based on curvelet domain features." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 3 (2021): 2595. http://dx.doi.org/10.11591/ijece.v11i3.pp2595-2603.

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Contrast is one of the most popular forms of distortion. Recently, the existing image quality assessment algorithms (IQAs) works focusing on distorted images by compression, noise and blurring. Reduced-reference image quality metric for contrast-changed images (RIQMC) and no reference-image quality assessment (NR-IQA) for contrast-distorted images (NR-IQA-CDI) have been created for CDI. NR-IQA-CDI showed poor performance in two out of three image databases, where the pearson correlation coefficient (PLCC) were only 0.5739 and 0.7623 in TID2013 and CSIQ database, respectively. Spatial domain fe

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Garcia Freitas, Pedro, Luísa da Eira, Samuel Santos, and Mylene Farias. "On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment." Journal of Imaging 4, no. 10 (2018): 114. http://dx.doi.org/10.3390/jimaging4100114.

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Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most rea

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Gu, Jie, Gaofeng Meng, Cheng Da, Shiming Xiang, and Chunhong Pan. "No-Reference Image Quality Assessment with Reinforcement Recursive List-Wise Ranking." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 8336–43. http://dx.doi.org/10.1609/aaai.v33i01.33018336.

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Opinion-unaware no-reference image quality assessment (NR-IQA) methods have received many interests recently because they do not require images with subjective scores for training. Unfortunately, it is a challenging task, and thus far no opinion-unaware methods have shown consistently better performance than the opinion-aware ones. In this paper, we propose an effective opinion-unaware NR-IQA method based on reinforcement recursive list-wise ranking. We formulate the NR-IQA as a recursive list-wise ranking problem which aims to optimize the whole quality ordering directly. During training, the

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Yin, Guanghao, Wei Wang, Zehuan Yuan, et al. "Content-Variant Reference Image Quality Assessment via Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 3 (2022): 3134–42. http://dx.doi.org/10.1609/aaai.v36i3.20221.

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Generally, humans are more skilled at perceiving differences between high-quality (HQ) and low-quality (LQ) images than directly judging the quality of a single LQ image. This situation also applies to image quality assessment (IQA). Although recent no-reference (NR-IQA) methods have made great progress to predict image quality free from the reference image, they still have the potential to achieve better performance since HQ image information is not fully exploited. In contrast, full-reference (FR-IQA) methods tend to provide more reliable quality evaluation, but its practicability is affecte

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Varga, Domonkos. "No-Reference Image Quality Assessment with Convolutional Neural Networks and Decision Fusion." Applied Sciences 12, no. 1 (2021): 101. http://dx.doi.org/10.3390/app12010101.

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No-reference image quality assessment (NR-IQA) has always been a difficult research problem because digital images may suffer very diverse types of distortions and their contents are extremely various. Moreover, IQA is also a very hot topic in the research community since the number and role of digital images in everyday life is continuously growing. Recently, a huge amount of effort has been devoted to exploiting convolutional neural networks and other deep learning techniques for no-reference image quality assessment. Since deep learning relies on a massive amount of labeled data, utilizing

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Ahmed, Basma, Osama A. Omer, Vivek Kumar Singh, Amal Rashed, and Mohamed Abdel-Nasser. "No-reference image quality assessment based on visual explanation images and deep transfer learning." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1521. http://dx.doi.org/10.11591/ijeecs.v36.i3.pp1521-1531.

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Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual e

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Basma, Ahmed Osama A. Omer Vivek Kumar Singh Amal Rashed Mohamed Abdel-Nasser. "No-reference image quality assessment based on visual explanation images and deep transfer learning." Indonesian Journal of Electrical Engineering and Computer Science 36, no. 3 (2024): 1521–31. https://doi.org/10.11591/ijeecs.v36.i3.pp1521-1531.

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Quantifying image quality in the absence of a reference image continues to be a challenge despite the introduction of numerous no-reference image quality assessments (NR-IQA) in recent years. Unlike most existing NRIQA methods, this paper proposes an efficient NR-IQA method based on deep visual interpretations. Specifically, the main components of the proposed method are: i) generating a pseudo-reference image (PRI) for the input distorted images, ii) employing a pretrained convolutional network to extract feature maps from the distorted image and the corresponding PRI, iii) producing visual e

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Gavrovska, Ana, Dragi Dujković, Andreja Samčović, Yuliya Golub, and Valery Starovoitov. "Quadratic fitting model in no-reference image quality assessment." Telfor Journal 15, no. 2 (2023): 32–37. http://dx.doi.org/10.5937/telfor2302032g.

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The perceptual quality of image is affected by distortions during compression, delivery and storage. Distortions also impact automatic image quality assessment (IQA) that needs to be highly correlated with subjective scores. In the absence of reference, which is a typical scenario in practice, no-reference (NR) metrics are necessary for quality measurements. Recently such methods are proposed, and they employ natural scene statistics (NSS). The experimental analysis performed in this paper takes into consideration two fitting or regression models of several NR-IQA metrics relying on different

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Varga, Domonkos. "No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features." Journal of Imaging 7, no. 7 (2021): 112. http://dx.doi.org/10.3390/jimaging7070112.

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The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extract

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Yan, Chenggang, Tong Teng, Yutao Liu, Yongbing Zhang, Haoqian Wang, and Xiangyang Ji. "Precise No-Reference Image Quality Evaluation Based on Distortion Identification." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 3s (2021): 1–21. http://dx.doi.org/10.1145/3468872.

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The difficulty of no-reference image quality assessment (NR IQA) often lies in the lack of knowledge about the distortion in the image, which makes quality assessment blind and thus inefficient. To tackle such issue, in this article, we propose a novel scheme for precise NR IQA, which includes two successive steps, i.e., distortion identification and targeted quality evaluation. In the first step, we employ the well-known Inception-ResNet-v2 neural network to train a classifier that classifies the possible distortion in the image into the four most common distortion types, i.e., Gaussian white

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Yu, Yi, Song Xia, Xun Lin, et al. "Backdoor Attacks Against No-Reference Image Quality Assessment Models via a Scalable Trigger." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 9 (2025): 9698–706. https://doi.org/10.1609/aaai.v39i9.33051.

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No-Reference Image Quality Assessment (NR-IQA), responsible for assessing the quality of a single input image without using any reference, plays a critical role in evaluating and optimizing computer vision systems, e.g., low-light enhancement. Recent research indicates that NR-IQA models are susceptible to adversarial attacks, which can significantly alter predicted scores with visually imperceptible perturbations. Despite revealing vulnerabilities, these attack methods have limitations, including high computational demands, untargeted manipulation, limited practical utility in white-box scena

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Stępień, Igor, and Mariusz Oszust. "A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images." Journal of Imaging 8, no. 6 (2022): 160. http://dx.doi.org/10.3390/jimaging8060160.

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No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA me

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Ye, Zhongchang, Xin Ye, and Zhonghua Zhao. "Hybrid No-Reference Quality Assessment for Surveillance Images." Information 13, no. 12 (2022): 588. http://dx.doi.org/10.3390/info13120588.

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Intelligent video surveillance (IVS) technology is widely used in various security systems. However, quality degradation in surveillance images (SIs) may affect its performance on vision-based tasks, leading to the difficulties in the IVS system extracting valid information from SIs. In this paper, we propose a hybrid no-reference image quality assessment (NR IQA) model for SIs that can help to identify undesired distortions and provide useful guidelines for IVS technology. Specifically, we first extract two main types of quality-aware features: the low-level visual features related to various

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Fu, Hao, Guojun Liu, Xiaoqin Yang, Lili Wei, and Lixia Yang. "Two Low-Level Feature Distributions Based No Reference Image Quality Assessment." Applied Sciences 12, no. 10 (2022): 4975. http://dx.doi.org/10.3390/app12104975.

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No reference image quality assessment (NR IQA) aims to develop quantitative measures to automatically and accurately estimate perceptual image quality without any prior information about the reference image. In this paper, we introduce two low-level feature distributions (TLLFD) based method for NR IQA. Different from the deep learning method, the proposed method characterizes image quality with the distributions of low-level features, thus it has few parameters, simple model, high efficiency, and strong robustness. First, the texture change of distorted image is extracted by the weighted hist

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Ismail, Taha Ahmed, Soong Der Chen, Jamil Norziana, and Afendee Mohamed Mohamad. "Improve of contrast-distorted image quality assessment based on convolutional neural networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5604–14. https://doi.org/10.11591/ijece.v9i6.pp5604-5614.

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Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced- reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design good handcrafted featur

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Guan, Xiaodi, Fan Li, and Lijun He. "Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels." Electronics 9, no. 2 (2020): 252. http://dx.doi.org/10.3390/electronics9020252.

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In this paper, we propose a no-reference image quality assessment (NR-IQA) approach towards authentically distorted images, based on expanding proxy labels. In order to distinguish from the human labels, we define the quality score, which is generated by using a traditional NR-IQA algorithm, as “proxy labels”. “Proxy” means that the objective results are obtained by computer after the extraction and assessment of the image features, instead of human judging. To solve the problem of limited image quality assessment (IQA) dataset size, we adopt a cascading transfer-learning method. First, we obt

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Varga, Domonkos. "No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features." Journal of Imaging 6, no. 8 (2020): 75. http://dx.doi.org/10.3390/jimaging6080075.

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The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, en

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Ahmed, Ismail Taha, Chen Soong Der, Norziana Jamil, and Mohamad Afendee Mohamed. "Improve of contrast-distorted image quality assessment based on convolutional neural networks." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 5604. http://dx.doi.org/10.11591/ijece.v9i6.pp5604-5614.

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<span lang="EN-US">Many image quality assessment algorithms (IQAs) have been developed during the past decade. However, most of them are designed for images distorted by compression, noise and blurring. There are very few IQAs designed specifically for Contrast Distorted Images (CDI), e.g. Reduced-reference Image Quality Metric for Contrast-changed images (RIQMC) and NR-IQA for Contrast-Distorted Images (NR-IQA-CDI). The existing NR-IQA-CDI relies on features designed by human or handcrafted features because considerable level of skill, domain expertise and efforts are required to design

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Stępień, Igor, and Mariusz Oszust. "No-Reference Quality Assessment of Pan-Sharpening Images with Multi-Level Deep Image Representations." Remote Sensing 14, no. 5 (2022): 1119. http://dx.doi.org/10.3390/rs14051119.

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The Pan-Sharpening (PS) techniques provide a better visualization of a multi-band image using the high-resolution single-band image. To support their development and evaluation, in this paper, a novel, accurate, and automatic No-Reference (NR) PS Image Quality Assessment (IQA) method is proposed. In the method, responses of two complementary network architectures in a form of extracted multi-level representations of PS images are employed as quality-aware information. Specifically, high-dimensional data are separately extracted from the layers of the networks and further processed with the Ker

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Ryu, Jihyoung. "Improved Image Quality Assessment by Utilizing Pre-Trained Architecture Features with Unified Learning Mechanism." Applied Sciences 13, no. 4 (2023): 2682. http://dx.doi.org/10.3390/app13042682.

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The purpose of the no-reference image quality assessment (NR-IQA) is to measure perceived image quality based on subjective judgments; however, due to the lack of a clean reference image, this is a complicated and unresolved challenge. Massive new IQA datasets have facilitated the creation of deep learning-based image quality measurements. We present a unique model to handle the NR-IQA challenge in this research by employing a hybrid strategy that leverages from pre-trained CNN model and the unified learning mechanism that extracts both local and non-local characteristics from the input patch.

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Varga, Domonkos. "A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors." Sensors 22, no. 18 (2022): 6775. http://dx.doi.org/10.3390/s22186775.

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Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired filters were applied to the color channels of an input image to enhance those statistical regularities in the image to which the human visual system is sensitive. From the obtained feature maps, the statistics of a wide range of local feature descriptors were extracted to compil

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Hu, Zhigang, Gege Yang, Zhe Du, Xiaodong Huang, Pujing Zhang, and Dechun Liu. "No-reference image quality assessment based on global awareness." PLOS ONE 19, no. 10 (2024): e0310206. http://dx.doi.org/10.1371/journal.pone.0310206.

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In the field of computer vision, the application of hand-crafted as well as computer-learning-based methods in the field of image quality assessment has yielded remarkable results. However, in the field of no-reference image distortion, it is still challenging to accurately perceive and determine the quality of an image. To address the difficulties of Image Quality Assessment (IQA) in the field of authentic distorted images, we consider the use of the Swin Transformer (ST) to extract features. To enable the model to focus on both spatial and channel information of features, we design a plug-an

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Ryu, Jihyoung. "A Visual Saliency-Based Neural Network Architecture for No-Reference Image Quality Assessment." Applied Sciences 12, no. 19 (2022): 9567. http://dx.doi.org/10.3390/app12199567.

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Deep learning has recently been used to study blind image quality assessment (BIQA) in great detail. Yet, the scarcity of high-quality algorithms prevents from developing them further and being used in a real-time scenario. Patch-based techniques have been used to forecast the quality of an image, but they typically award the picture quality score to an individual patch of the image. As a result, there would be a lot of misleading scores coming from patches. Some regions of the image are important and can contribute highly toward the right prediction of its quality. To prevent outlier regions,

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Abdalmajeed, Saifeldeen, and Jiao Shuhong. "Using the Natural Scenes’ Edges for Assessing Image Quality Blindly and Efficiently." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/389504.

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Two real blind/no-reference (NR) image quality assessment (IQA) algorithms in the spatial domain are developed. To measure image quality, the introduced approach uses an unprecedented concept for gathering a set of novel features based on edges of natural scenes. The enhanced sensitivity of the human eye to the information carried by edge and contour of an image supports this claim. The effectiveness of the proposed technique in quantifying image quality has been studied. The gathered features are formed using both Weibull distribution statistics and two sharpness functions to devise two separ

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Mahmood, Saifeldeen Abdalmajeed. "Three Different Features Based Metric To Assess Image Quality Blindly." FES Journal of Engineering Sciences 8, no. 2 (2020): 97–103. http://dx.doi.org/10.52981/fjes.v8i2.121.

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Abstract When creating image quality assessment metric (IQA) no confirmation all distortion types are available. Non-specific distortion blind/no-reference (NR) IQA algorithms mostly need prior knowledge about anticipated distortions. This paper introduce a generic and distortion unaware (DU) approach for IQA with No Reference (NR). The approach uses three different measuring features which are initiated from the gist of natural scenes (NS) using Log-derivatives of the parameters; a general Gaussian distribution model, two sharpness functions, and Weibull distribution. All features were analyz

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Yang, Yang, Chang Liu, Hui Wu, and Dingguo Yu. "A quality assessment algorithm for no-reference images based on transfer learning." PeerJ Computer Science 11 (January 31, 2025): e2654. https://doi.org/10.7717/peerj-cs.2654.

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Image quality assessment (IQA) plays a critical role in automatically detecting and correcting defects in images, thereby enhancing the overall performance of image processing and transmission systems. While research on reference-based IQA is well-established, studies on no-reference image IQA remain underdeveloped. In this article, we propose a novel no-reference IQA algorithm based on transfer learning (IQA-NRTL). This algorithm leverages a deep convolutional neural network (CNN) due to its ability to effectively capture multi-scale semantic information features, which are essential for repr

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LU, WEN, LIHUO HE, WENJIAN TANG, FEI GAO, and WEILONG HOU. "A NOVEL COMPRESSED IMAGES QUALITY METRIC." International Journal of Image and Graphics 11, no. 02 (2011): 281–92. http://dx.doi.org/10.1142/s021946781100410x.

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As the performance indicator of the image processing algorithms or systems, image quality assessment (IQA) has attracted the attention of many researchers. Aiming to the widely used compression standards, JPEG and JPEG2000, we propose a new no reference (NR) metric for compressed images to do IQA. This metric exploits the causes of distortion by JPEG and JPEG2000, employs the directional discrete cosine transform (DDCT) to obtain the detail and directional information of the images and incorporates with the visual perception to obtain the image quality index. Experimental results show that the

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Besma, Sadou, Lahoulou Atidel, Bouden Toufik, R. Avila Anderson, H. Falk Tiago, and Akhtar Zahid. "Free- Reference Image Quality Assessment Framework Using Metrics Fusion and Dimensionality Reduction." Signal & Image Processing: An International Journal (SIPIJ) 10, no. 5 (2019): 1–14. https://doi.org/10.5281/zenodo.3531477.

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This paper focuses on no-reference image quality assessment(NR-IQA)metrics. In the literature, a wide range of algorithms are proposed to automatically estimate the perceived quality of visual data. However, most of them are not able to effectively quantify the various degradations and artifacts that the image may undergo. Thus, merging of diverse metrics operating in different information domains is hoped to yield better performances, which is the main theme of the proposed work. In particular, the metric proposed in this paper is based on three well-known NR-IQA objective metrics that depend

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Nam, Woongchan, Taehyun Youn, and Chunghun Ha. "No-Reference Image Quality Assessment with Moving Spectrum and Laplacian Filter for Autonomous Driving Environment." Vehicles 7, no. 1 (2025): 8. https://doi.org/10.3390/vehicles7010008.

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The increasing integration of autonomous driving systems into modern vehicles heightens the significance of Image Quality Assessment (IQA), as it pertains directly to vehicular safety. In this context, the development of metrics that can emulate the Human Visual System (HVS) in assessing image quality assumes critical importance. Given that blur is often the primary aberration in images captured by aging or deteriorating camera sensors, this study introduces a No-Reference (NR) IQA model termed BREMOLA (Blind/Referenceless Model via Moving Spectrum and Laplacian Filter). This model is designed

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Ullah, Hayat, Muhammad Irfan, Kyungjin Han, and Jong Weon Lee. "DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment." Sensors 20, no. 22 (2020): 6457. http://dx.doi.org/10.3390/s20226457.

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Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable perf

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Wang, Yue, Zeng Gang Lin, and Zi Cheng Liao. "Image Quality Assessment Based on Region of Interest." Applied Mechanics and Materials 596 (July 2014): 350–54. http://dx.doi.org/10.4028/www.scientific.net/amm.596.350.

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In this paper a new No-Reference (NR) image quality assessment (IQA) method based on the point wise statistics of local normalized luminance signals using region of interest (ROI) processing is proposed. This algorithm firstly extracts the ROI which is relative to human subjectivity by using the image gradient and phase congruency, and then extracts the image quality feature in spatial domain. Particularly, most of the present IQA methods mainly focus on predicting the image quality with respect to human perception, yet, in some other image domains, the final receiver of a digital image may no

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Han, Lintao, Hengyi Lv, Yuchen Zhao, et al. "Conv-Former: A Novel Network Combining Convolution and Self-Attention for Image Quality Assessment." Sensors 23, no. 1 (2022): 427. http://dx.doi.org/10.3390/s23010427.

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To address the challenge of no-reference image quality assessment (NR-IQA) for authentically and synthetically distorted images, we propose a novel network called the Combining Convolution and Self-Attention for Image Quality Assessment network (Conv-Former). Our model uses a multi-stage transformer architecture similar to that of ResNet-50 to represent appropriate perceptual mechanisms in image quality assessment (IQA) to build an accurate IQA model. We employ adaptive learnable position embedding to handle images with arbitrary resolution. We propose a new transformer block (TB) by taking ad

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Zhang, Run, and Yongbin Wang. "Natural Image Quality Assessment Based on Visual Biological Cognitive Mechanism." International Journal of Software Innovation 7, no. 1 (2019): 1–26. http://dx.doi.org/10.4018/ijsi.2019010101.

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With the focus of the main problems in no-reference natural image quality assessment (NR-IQA), the researchers propose a more universal, efficient and integrated resolution based on visual biological cognitive mechanism. First, the authors bring up an inspiring visual cognitive computing model (IVCCM) on the basis of visual heuristic principles. Second, the authors put forward an asymmetric generalized gaussian mixture distribution model (AGGMD), and the model can describe the probability distribution density of the images more precisely. Third, the authors extract the quality-aware multiscale

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Cui, Yueli. "No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion." Entropy 22, no. 3 (2020): 344. http://dx.doi.org/10.3390/e22030344.

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Image quality assessment (IQA) aims to devise computational models to evaluate image quality in a perceptually consistent manner. In this paper, a novel no-reference image quality assessment model based on dual-domain feature fusion is proposed, dubbed as DFF-IQA. Firstly, in the spatial domain, several features about weighted local binary pattern, naturalness and spatial entropy are extracted, where the naturalness features are represented by fitting parameters of the generalized Gaussian distribution. Secondly, in the frequency domain, the features of spectral entropy, oriented energy distri

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Stępień, Igor, Rafał Obuchowicz, Adam Piórkowski, and Mariusz Oszust. "Fusion of Deep Convolutional Neural Networks for No-Reference Magnetic Resonance Image Quality Assessment." Sensors 21, no. 4 (2021): 1043. http://dx.doi.org/10.3390/s21041043.

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The quality of magnetic resonance images may influence the diagnosis and subsequent treatment. Therefore, in this paper, a novel no-reference (NR) magnetic resonance image quality assessment (MRIQA) method is proposed. In the approach, deep convolutional neural network architectures are fused and jointly trained to better capture the characteristics of MR images. Then, to improve the quality prediction performance, the support vector machine regression (SVR) technique is employed on the features generated by fused networks. In the paper, several promising network architectures are introduced,

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Qian, Qi, and Qingbing Sang. "No-reference image quality assessment based on automatic machine learning." ITM Web of Conferences 45 (2022): 01034. http://dx.doi.org/10.1051/itmconf/20224501034.

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In different applications in deep learning, due to different required features, it is necessary to design specialized Neural Network structure. However, the design of the structure largely depends on the relevant subject knowledge of researchers and lots of experiments, resulting in huge waste of manpower. Therefore, in the field of Image Quality Assessment (IQA), the authors propose a method to apply Neural Architecture Search (NAS) to IQA. Mainly through the Differentiable Architecture Search algorithm, the structure of the modular Neural Network unit is searched by the stochastic gradient d

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Chandler, Damon M. "Seven Challenges in Image Quality Assessment: Past, Present, and Future Research." ISRN Signal Processing 2013 (February 6, 2013): 1–53. http://dx.doi.org/10.1155/2013/905685.

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Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable pr

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Varga, Domonkos. "No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features." Journal of Imaging 8, no. 6 (2022): 173. http://dx.doi.org/10.3390/jimaging8060173.

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With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic disto

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Gupta, Praful, Christos Bampis, Jack Glover, Nicholas Paulter, and Alan Bovik. "Multivariate Statistical Approach to Image Quality Tasks." Journal of Imaging 4, no. 10 (2018): 117. http://dx.doi.org/10.3390/jimaging4100117.

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Many existing natural scene statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here, we propose a multivariate model of natural image coefficients expressed in the bandpass spatial domain that has the potential to capture higher order correlations that may be induced by the presence of distortions. We analyze how the parameters of the multivariate model are affected by different distortion types, and we show their ability to capture distortion-

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Gao, Guoqing, Lingxiao Li, Hao Chen, et al. "No-Reference Quality Assessment of Extended Target Adaptive Optics Images Using Deep Neural Network." Sensors 24, no. 1 (2023): 1. http://dx.doi.org/10.3390/s24010001.

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This paper proposes a supervised deep neural network model for accomplishing highly efficient image quality assessment (IQA) for adaptive optics (AO) images. The AO imaging systems based on ground-based telescopes suffer from residual atmospheric turbulence, tracking error, and photoelectric noise, which can lead to varying degrees of image degradation, making image processing challenging. Currently, assessing the quality and selecting frames of AO images depend on either traditional IQA methods or manual evaluation by experienced researchers, neither of which is entirely reliable. The propose

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Gu, Ke, Guangtao Zhai, Xiaokang Yang, and Wenjun Zhang. "No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation." Journal of Electrical and Computer Engineering 2012 (2012): 1–12. http://dx.doi.org/10.1155/2012/436031.

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The last decade has seen a booming of the applications of stereoscopic images/videos and the corresponding technologies, such as 3D modeling, reconstruction, and disparity estimation. However, only a very limited number of stereoscopic image quality assessment metrics was proposed through the years. In this paper, we propose a new no-reference stereoscopic image quality assessment algorithm based on the nonlinear additive model, ocular dominance model, and saliency based parallax compensation. Our studies using the Toyama database result in three valuable findings. First, quality of the stereo

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Lei, Shu, Huang Zijian, Yan Jiebin, and Fei Fengchang. "Super Resolution Image Visual Quality Assessment Based on Feature Optimization." Computational Intelligence and Neuroscience 2022 (June 20, 2022): 1–10. http://dx.doi.org/10.1155/2022/1263348.

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Most existing no-referenced image quality assessment (NR-IQA) algorithms need to extract features first and then predict image quality. However, only a small number of features work in the model, and the rest will degrade the model performance. Consequently, an NR-IQA framework based on feature optimization is proposed to solve this problem and apply to the SR-IQA field. In this study, we designed a feature engineering method to solve this problem. Specifically, the features associate with the SR images were first collected and aggregated. Furthermore, several advanced feature selection algori

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Liu, Jiapeng, Yi Liu, and Qiuping Jiang. "Delving into Underwater Image Utility: Benchmark Dataset and Prediction Model." Remote Sensing 17, no. 11 (2025): 1906. https://doi.org/10.3390/rs17111906.

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High-quality underwater images are essential for both human visual perception and machine analysis in marine vision applications. Although significant progress has been achieved in Underwater Image Quality Assessment (UIQA), almost all existing UIQA methods focus on the visual perception-oriented image quality issue and cannot be used to gauge the utility of underwater images for the use in machine vision applications. To address this issue, in this work, we focus on the problem of automatic underwater image utility assessment (UIUA). On the one hand, we first construct a large-scale Object De

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Varga, Domonkos. "Multi-Pooled Inception Features for No-Reference Image Quality Assessment." Applied Sciences 10, no. 6 (2020): 2186. http://dx.doi.org/10.3390/app10062186.

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Image quality assessment (IQA) is an important element of a broad spectrum of applications ranging from automatic video streaming to display technology. Furthermore, the measurement of image quality requires a balanced investigation of image content and features. Our proposed approach extracts visual features by attaching global average pooling (GAP) layers to multiple Inception modules of on an ImageNet database pretrained convolutional neural network (CNN). In contrast to previous methods, we do not take patches from the input image. Instead, the input image is treated as a whole and is run

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Hu, Kai, Yanwen Zhang, Feiyu Lu, Zhiliang Deng, and Yunping Liu. "An Underwater Image Enhancement Algorithm Based on MSR Parameter Optimization." Journal of Marine Science and Engineering 8, no. 10 (2020): 741. http://dx.doi.org/10.3390/jmse8100741.

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The quality of underwater images is often affected by the absorption of light and the scattering and diffusion of floating objects. Therefore, underwater image enhancement algorithms have been widely studied. In this area, algorithms based on Multi-Scale Retinex (MSR) represent an important research direction. Although the visual quality of underwater images can be improved to some extent, the enhancement effect is not good due to the fact that the parameters of these algorithms cannot adapt to different underwater environments. To solve this problem, based on classical MSR, we propose an unde

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