Log in

Relevant bibliographies by topics / No-Reference image quality assessment (NR-IQA)

Contents

Journal articles
Dissertations / Theses
Book chapters
Conference papers

Academic literature on the topic 'No-Reference image quality assessment (NR-IQA)'

Author: Grafiati

Published: 7 July 2024

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'No-Reference image quality assessment (NR-IQA).'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

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

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

2

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

3

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

4

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

5

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

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

6

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

7

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

8

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

9

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

10

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.

Full text

Abstract:

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

APA, Harvard, Vancouver, ISO, and other styles

More sources

Dissertations / Theses on the topic "No-Reference image quality assessment (NR-IQA)"

1

Hettiarachchi, Don Lahiru Nirmal Manikka. "An Accelerated General Purpose No-Reference Image Quality Assessment Metric and an Image Fusion Technique." University of Dayton / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1470048998.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Nguyen, Tan-Sy. "A smart system for processing and analyzing gastrointestinal abnormalities in wireless capsule endoscopy." Electronic Thesis or Diss., Paris 13, 2023. http://www.theses.fr/2023PA131052.

Full text

Abstract:

Dans cette thèse, nous abordons les défis liés à l'identification et au diagnostic des lésions pathologiques dans le tractus gatro-intestinal (GI). L'analyse des quantités massives d'informations visuelles obtenues par une capsule vidéo-endoscopique (CVE) qui est un excellent outil pour visualiser et examiner le tractus GI y compris l'intestin grêle, représente une charge considérable pour les cliniciens, entraînant un risque accru de diagnostic erroné. Afin de palier à ce problème, nous développons un système intelligent capable de détecter et d'identifier automatiquement diverses pathologies

APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "No-Reference image quality assessment (NR-IQA)"

1

Jamshidi Avanaki, Nasim, Abhijay Ghildyal, Nabajeet Barman, and Saman Zadtootaghaj. "LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91838-4_20.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Patil, Milind S., and Pradip B. Mane. "An Efficient Approach for No Reference Image Quality Assessment (NR-IQA) Index Using Autoencoder-Based Regression Model (ARM)." In Advances in Science, Technology & Innovation. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-68038-0_16.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Ahmed, Basma, Mohamed Abdel-Nasser, Osama A. Omer, Amal Rashed, and Domenec Puig. "No-Reference Digital Image Quality Assessment Based on Structure Similarity." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210156.

Full text

Abstract:

Blind or non-referential image quality assessment (NR-IQA) indicates the problem of evaluating the visual quality of an image without any reference, Therefore, the need to develop a new measure that does not depend on the reference pristine image. This paper presents a NR-IQA method based on restoration scheme and a structural similarity index measure (SSIM). Specifically, we use blind restoration schemes for blurred images by reblurring the blurred image and then we use it as a reference image. Finally, we use the SSIM as a full reference metric. The experiments performed on standard test ima

APA, Harvard, Vancouver, ISO, and other styles

4

Ahmed, Basma, Osama A. Omer, Amal Rashed, Domenec Puig, and Mohamed Abdel-Nasser. "Referenceless Image Quality Assessment Utilizing Deep Transfer-Learned Features." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220345.

Full text

Abstract:

Image quality assessment (IQA) algorithms are critical for determining the quality of high-resolution photographs. This work proposes a hybrid NR IQA approach that uses deep transfer learning to enhance classic NR IQA with deep learning characteristics. Firstly, we simulate a pseudo reference image (PRI) from the input image. Then, we used a pre-trained inception-v3 deep feature extractor to generate the feature maps from the input distorted image and PRI. The distance between the feature maps of the input distorted image and PRI are measured using the local structural similarity (LSS) method.

APA, Harvard, Vancouver, ISO, and other styles

5

Abdelouahad, Abdelkaher Ait, Mohammed El Hassouni, Hocine Cherifi, and Driss Aboutajdine. "A New Image Distortion Measure Based on Natural Scene Statistics Modeling." In Geographic Information Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2038-4.ch037.

Full text

Abstract:

In the field of Image Quality Assessment (IQA), this paper examines a Reduced Reference (RRIQA) measure based on the bi-dimensional empirical mode decomposition. The proposed measure belongs to Natural Scene Statistics (NSS) modeling approaches. First, the reference image is decomposed into Intrinsic Mode Functions (IMF); the authors then use the Generalized Gaussian Density (GGD) to model IMF coefficients distribution. At the receiver side, the same number of IMF is computed on the distorted image, and then the quality assessment is done by fitting error between the IMF coefficients histogram

APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "No-Reference image quality assessment (NR-IQA)"

1

Sujana, D. Swainson, D. Peter Augustine, and D. Sheefa Ruby Grace. "Full Reference Image Quality Assessment (FR-IQA) of Pre-processed Structural Magnetic Resonance Images." In 2024 IEEE International Conference on Contemporary Computing and Communications (InC4). IEEE, 2024. http://dx.doi.org/10.1109/inc460750.2024.10649151.

Full text

APA, Harvard, Vancouver, ISO, and other styles

2

Ariffin, Syed Mohd Zahid Syed Zainal, and Nursuriati Jamil. "Illumination Classification based on No-Reference Image Quality Assessment (NR-IQA)." In the 2019 Asia Pacific Information Technology Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3314527.3314529.

Full text

APA, Harvard, Vancouver, ISO, and other styles

3

Da Silva, Renato, Luiz Brito, Marcelo Albertini, Marcelo Do Nascimento, and André Backes. "Using CNNs for Quality Assessment of No-Reference and Full-Reference Compressed-Video Frames." In Workshop de Visão Computacional. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/wvc.2020.13484.

Full text

Abstract:

For videos to be streamed, they have to be coded and sent to users as signals that are decoded back to be reproduced. This coding-decoding process may result in distortion that can bring differences in the quality perception of the content, consequently, influencing user experience. The approach proposed by Bosse et al. [1] suggests an Image Quality Assessment (IQA) method using an automated process. They use image datasets prelabeled with quality scores to perform a Convolutional Neural Network (CNN) training. Then, based on the CNN models, they are able to perform predictions of image qualit

APA, Harvard, Vancouver, ISO, and other styles

4

Lin, Kwan-Yee, and Guanxiang Wang. "Hallucinated-IQA: No-Reference Image Quality Assessment via Adversarial Learning." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2018. http://dx.doi.org/10.1109/cvpr.2018.00083.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Gil, Adriano, Aasim Khurshid, Juliana Postal, and Thiago Figueira. "Visual assessment of equirectangular images for virtual reality applications In Unity." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8337.

Full text

Abstract:

Virtual Reality (VR) applications provide an immersive experience when using panoramic images that contain a 360-degree view of the scene. Currently, the equirectangular image format is the widely used pattern to represent these panoramic images. The development of a virtual reality viewer of panoramic images should consider several parameters that define the quality of the rendered image. Such parameters include resolution configurations, texture-to-objects mappings and deciding from different rendering approach, but to select the optimal value of these parameters, visual quality analysis is

APA, Harvard, Vancouver, ISO, and other styles

6

Narsaiah, D., R. Surender Reddy, Aruna Kokkula, P. Anil Kumar, and A. Karthik. "A Novel Full Reference-Image Quality Assessment (FR-IQA) for Adaptive Visual Perception Improvement." In 2021 6th International Conference on Inventive Computation Technologies (ICICT). IEEE, 2021. http://dx.doi.org/10.1109/icict50816.2021.9358610.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Zaytoon, Mohamed, and Marwan Torki. "The Effect of Non-Reference Point Cloud Quality Assessment (NR-PCQA) Loss on 3D Scene Reconstruction from a Single Image." In 2023 IEEE Symposium on Computers and Communications (ISCC). IEEE, 2023. http://dx.doi.org/10.1109/iscc58397.2023.10218197.

Full text

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!