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Journal articles on the topic 'Image quality assessment metric'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Berezsky, O. M., M. O. Berezkyi, M. O. Dombrovskyi, P. B. Liashchynskyi, and G. M. Melnyk. "COMBINED METRIC FOR EVALUATING THE QUALITY OF SYNTHESIZED BIOMEDICAL IMAGES." Radio Electronics, Computer Science, Control, no. 2 (June 29, 2025): 168–81. https://doi.org/10.15588/1607-3274-2025-2-15.

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Context. This study addresses the problem of developing a new metric for evaluating the quality of synthesized images. The relevance of this problem is explained by the need for assessing the quality of artificially generated images. Additionally, the study highlights the potential of biomedical image synthesis based on diffusion models. The research results can be applied for biomedical image generation and quantitative quality assessment of synthesized images.Objective. The aim of this study is to develop a combined metric and an algorithm for biomedical image synthesis to assess the quality

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Bouida, Ahmed, Mustapha Khelifi, Mohammed Beladgham, and Fatima-Zohra Hamlili. "Monte Carlo Optimization of a Combined Image Quality Assessment for Compressed Images Evaluation." Traitement du Signal 38, no. 2 (2021): 281–89. http://dx.doi.org/10.18280/ts.380204.

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In image processing, using compression is very important in various applications, especially those using data quantities in transmission and storing. This importance becomes most required with the evolution of image quantities and the big data systems explosion. The image compression allows reducing the required binary volume of image data by encoding the image for transmission goal or database saving. The principal problem with image compression when reducing its size is the degradation that enters the image. This degradation can affect the quality of use of the compressed image. To evaluate

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Frackiewicz, Mariusz, Łukasz Machalica, and Henryk Palus. "New Combined Metric for Full-Reference Image Quality Assessment." Symmetry 16, no. 12 (2024): 1622. https://doi.org/10.3390/sym16121622.

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In recent years, many new metrics highly correlated with the Mean Opinion Score (MOS) have been proposed for assessing image quality through Full-Reference Image Quality Assessment (FR-IQA) methods, such as MDSI, HPSI, and GMSD. Eight of these selected metrics, which compare reference and distorted images in a symmetrical manner, are briefly described in this article, and their performance is evaluated using correlation criteria (PLCC, SROCC, and KROCC), as well as RMSE. The aim of this paper is to develop a new, efficient quality index based on a combination of several high-performance metric

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Hamoudi, Radhi Sh. "Study Algorithms which Assessed Quality of the Blurred Images." University of Thi-Qar Journal of Science 4, no. 4 (2014): 79–85. http://dx.doi.org/10.32792/utq/utjsci/v4i4.671.

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No-reference measurement of blurring artifacts in images is a difficult problem in image quality assessment field. In this paper, we present a no-reference blur metric to measure the quality of the images. These images are degraded using Gaussian blurring. Suggestion method depends on developing the Mean of Locally Standard deviation and Mean of the image (MLSD) model, this method is called Blur Quality Metric (BQM) and it calculates from numerical integral of the function in this model. And the BQM is compared with the No-reference Perceptual Blur Metrics (PBM) and the Entropy of the First De

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Berga, David, Pau Gallés, Katalin Takáts, et al. "QMRNet: Quality Metric Regression for EO Image Quality Assessment and Super-Resolution." Remote Sensing 15, no. 9 (2023): 2451. http://dx.doi.org/10.3390/rs15092451.

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The latest advances in super-resolution have been tested with general-purpose images such as faces, landscapes and objects, but mainly unused for the task of super-resolving earth observation images. In this research paper, we benchmark state-of-the-art SR algorithms for distinct EO datasets using both full-reference and no-reference image quality assessment metrics. We also propose a novel Quality Metric Regression Network (QMRNet) that is able to predict the quality (as a no-reference metric) by training on any property of the image (e.g., its resolution, its distortions, etc.) and also able

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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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Yu, Jianping, Gang Li, Shaohui Wang, and Ling Lin. "Image quality assessment metric for frame accumulated image." Review of Scientific Instruments 89, no. 1 (2018): 013703. http://dx.doi.org/10.1063/1.5020715.

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Okarma, Krzysztof. "Current Trends and Advances in Image Quality Assessment." Elektronika ir Elektrotechnika 25, no. 3 (2019): 77–84. http://dx.doi.org/10.5755/j01.eie.25.3.23681.

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Image quality assessment (IQA) is one of the constantly active areas of research in computer vision. Starting from the idea of Universal Image Quality Index (UIQI), followed by well-known Structural Similarity (SSIM) and its numerous extensions and modifications, through Feature Similarity (FSIM) towards combined metrics using the multi-metric fusion approach, the development of image quality assessment is still in progress. Nevertheless, regardless of new databases and the potential use of deep learning methods, some challenges remain still up to date. Some of the IQA metrics can also be used

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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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Sandić-Stanković, Dragana, Dragan Kukolj, and Patrick Le Callet. "Multi–Scale Synthesized View Assessment Based on Morphological Pyramids." Journal of Electrical Engineering 67, no. 1 (2016): 3–11. http://dx.doi.org/10.1515/jee-2016-0001.

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Abstract The Depth-Image-Based-Rendering (DIBR) algorithms used for 3D video applications introduce geometric distortions affecting the edge coherency in the synthesized images. In order to better deal with specific geometric distortions in the DIBR synthesized images, we propose full-reference metric based on multi-scale pyramid decompositions using morphological filters. The non-linear morphological filters used in multi-scale image decompositions maintain important geometric information such as edges across different resolution levels. We show that PSNR has particularly good agreement with

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Lamichhane, Kamal, Marco Carli, and Federica Battisti. "Saliency-based deep blind image quality assessment." Electronic Imaging 2021, no. 9 (2021): 225–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-225.

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Assessing the quality of images is a challenging task. To achieve this goal, the images must be evaluated by a pool of subjects following a well-defined assessment protocol or an objective quality metric must be defined. In this contribution, an objective metric based on neural networks is proposed. The model takes into account the human vision system by computing a saliency map of the image under test. The system is based on two modules: the first one is trained using normalized distorted images. It learns the features from the original and the distorted images and the estimated saliency map.

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Han, Zongxi, Yutao Liu, Rong Xie, and Guangtao Zhai. "Image Quality Assessment for Realistic Zoom Photos." Sensors 23, no. 10 (2023): 4724. http://dx.doi.org/10.3390/s23104724.

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New CMOS imaging sensor (CIS) techniques in smartphones have helped user-generated content dominate our lives over traditional DSLRs. However, tiny sensor sizes and fixed focal lengths also lead to more grainy details, especially for zoom photos. Moreover, multi-frame stacking and post-sharpening algorithms would produce zigzag textures and over-sharpened appearances, for which traditional image-quality metrics may over-estimate. To solve this problem, a real-world zoom photo database is first constructed in this paper, which includes 900 tele-photos from 20 different mobile sensors and ISPs.

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DENG, CHENG, JIE LI, YIFAN ZHANG, DONGYU HUANG, and LINGLING AN. "AN IMAGE QUALITY METRIC BASED ON BIOLOGICALLY INSPIRED FEATURE MODEL." International Journal of Image and Graphics 11, no. 02 (2011): 265–79. http://dx.doi.org/10.1142/s0219467811004093.

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Objective image quality assessment (IQA) metrics have been widely applied to imaging systems to preserve and enhance the perceptual quality of images being processed and transmitted. In this paper, we present a novel IQA metric based on biologically inspired feature model (BIFM) and structural similarity index (SSIM). The SSIM index map is first generated through the well-known IQA metric SSIM between the reference image and the distorted image. Then, saliency map of the distorted image is extracted via BIF to define the most salient image locations. Finally, according to the saliency map, a f

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Tomás, Julián Espinosa, Jorge Pérez Rodríguez, David Más Candela, Carmen Vázquez Ferri, and Esther Perales. "Objective Prediction of Human Visual Acuity Using Image Quality Metrics." Applied Sciences 13, no. 10 (2023): 6350. http://dx.doi.org/10.3390/app13106350.

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This work addresses the objective prediction of human uncorrected decimal visual acuity, an unsolved challenge due to the contribution of both physical and neural factors. An alternative approach to assess the image quality of the human visual system can be addressed from the image and video processing perspective. Human tolerance to image degradation is quantified by mean opinion scores, and several image quality assessment algorithms are used to maintain, control, and improve the quality of processed images. The aberration map of the eye is used to obtain the degraded theoretical image from

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Shen, Liquan, Yang Yao, Xianqiu Geng, Ruigang Fang, and Dapeng Wu. "A Novel No-Reference Quality Assessment Metric for Stereoscopic Images with Consideration of Comprehensive 3D Quality Information." Sensors 23, no. 13 (2023): 6230. http://dx.doi.org/10.3390/s23136230.

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Recently, stereoscopic image quality assessment has attracted a lot attention. However, compared with 2D image quality assessment, it is much more difficult to assess the quality of stereoscopic images due to the lack of understanding of 3D visual perception. This paper proposes a novel no-reference quality assessment metric for stereoscopic images using natural scene statistics with consideration of both the quality of the cyclopean image and 3D visual perceptual information (binocular fusion and binocular rivalry). In the proposed method, not only is the quality of the cyclopean image consid

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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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Chandrakanth, T., and B. Sandhya. "Analysis of SSIM based Quality Assessment across Color Channels of Images." International Journal of System Dynamics Applications 4, no. 3 (2015): 30–42. http://dx.doi.org/10.4018/ijsda.2015070102.

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Advances in imaging and computing hardware have led to an explosion in the use of color images in image processing, graphics and computer vision applications across various domains such as medical imaging, satellite imagery, document analysis and biometrics to name a few. However, these images are subjected to a wide variety of distortions during its acquisition, subsequent compression, transmission, processing and then reproduction, which degrade their visual quality. Hence objective quality assessment of color images has emerged as one of the essential operations in image processing. During

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Irshad, Muhammad, Camilo Sanchez-Ferreira, Sana Alamgeer, Carlos H. Llanos, and Mylène C. Q. Farias. "No-reference Image Quality Assessment of Underwater Images Using Multi-Scale Salient Local Binary Patterns." Electronic Imaging 2021, no. 9 (2021): 265–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-265.

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Images acquired in underwater scenarios may contain severe distortions due to light absorption and scattering, color distortion, poor visibility, and contrast reduction. Because of these degradations, researchers have proposed several algorithms to restore or enhance underwater images. One way to assess these algorithms’ performance is to measure the quality of the restored/enhanced underwater images. Unfortunately, since reference (pristine) images are often not available, designing no-reference (blind) image quality metrics for this type of scenario is still a challenge. In fact, although th

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Okarma, Krzysztof, Wojciech Chlewicki, Mateusz Kopytek, Beata Marciniak, and Vladimir Lukin. "Entropy-Based Combined Metric for Automatic Objective Quality Assessment of Stitched Panoramic Images." Entropy 23, no. 11 (2021): 1525. http://dx.doi.org/10.3390/e23111525.

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Quality assessment of stitched images is an important element of many virtual reality and remote sensing applications where the panoramic images may be used as a background as well as for navigation purposes. The quality of stitched images may be decreased by several factors, including geometric distortions, ghosting, blurring, and color distortions. Nevertheless, the specificity of such distortions is different than those typical for general-purpose image quality assessment. Therefore, the necessity of the development of new objective image quality metrics for such type of emerging applicatio

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Rajagopal, Heshalini, Norrima Mokhtar, Anis Salwa Mohd Khairuddin, et al. "A No-Reference Image Quality Assessment Metric for Wood Images." Journal of Robotics, Networking and Artificial Life 8, no. 2 (2021): 127. http://dx.doi.org/10.2991/jrnal.k.210713.012.

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Ruikar, Jayesh, Ashoke Sinha, and Saurabh Chaudhury. "Image Quality Assessment Using Edge Correlation." International Journal of Electronics and Telecommunications 63, no. 1 (2017): 99–107. http://dx.doi.org/10.1515/eletel-2017-0014.

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Abstract In literature, oriented filters are used for low-level vision tasks. In this paper, we propose use of steerable Gaussian filter in image quality assessment. Human visual system is more sensitive to multidirectional edges present in natural images. The most degradation in image quality is caused due to its edges. In this work, an edge based metric termed as steerable Gaussian filtering (SGF) quality index is proposed as objective measure for image quality assessment. The performance of the proposed technique is evaluated over multiple databases. The experimental result shows that propo

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Maruyama, Sho, Haruyuki Watanabe, and Masayuki Shimosegawa. "An image quality assessment index based on image features and keypoints for X-ray CT images." PLOS ONE 19, no. 7 (2024): e0304860. http://dx.doi.org/10.1371/journal.pone.0304860.

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Optimization tasks in diagnostic radiological imaging require objective quantitative metrics that correlate with the subjective perception of observers. However, although one such metric, the structural similarity index (SSIM), is popular, it has limitations across various aspects in its application to medical images. In this study, we introduce a novel image quality evaluation approach based on keypoints and their associated unique image feature values, focusing on developing a framework to address the need for robustness and interpretability that are lacking in conventional methodologies. Th

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Li, Yuyan, Yubo Dong, Haoyong Li, Danhua Liu, Fang Xue, and Dahua Gao. "No-Reference Hyperspectral Image Quality Assessment via Ranking Feature Learning." Remote Sensing 16, no. 10 (2024): 1657. http://dx.doi.org/10.3390/rs16101657.

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In hyperspectral image (HSI) reconstruction tasks, due to the lack of ground truth in real imaging processes, models are usually trained and validated on simulation datasets and then tested on real measurements captured by real HSI imaging systems. However, due to the gap between the simulation imaging process and the real imaging process, the best model validated on the simulation dataset may fail on real measurements. To obtain the best model for the real-world task, it is crucial to design a suitable no-reference HSI quality assessment metric to reflect the reconstruction performance of dif

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Kwan, Chiman, and Bence Budavari. "A New Blind Video Quality Metric for Assessing Different Turbulence Mitigation Algorithms." Electronics 10, no. 18 (2021): 2277. http://dx.doi.org/10.3390/electronics10182277.

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Although many algorithms have been proposed to mitigate air turbulence in optical videos, there do not seem to be consistent blind video quality assessment metrics that can reliably assess different approaches. Blind video quality assessment metrics are necessary because many videos containing air turbulence do not have ground truth. In this paper, a simple and intuitive blind video quality assessment metric is proposed. This metric can reliably and consistently assess various turbulent mitigation algorithms for optical videos. Experimental results using more than 10 videos in the literature s

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Okarma, Krzysztof, and Mateusz Kopytek. "Improved Combined Metric for Automatic Quality Assessment of Stitched Images." Applied Sciences 12, no. 20 (2022): 10284. http://dx.doi.org/10.3390/app122010284.

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An automatic quality assessment of stitched images is an essential task in image analysis and is particularly useful not only in the creation of general-purpose panoramic images but also in terrain exploration and mapping made by mobile robots and drones. In Visual Simultaneous Localization and Mapping (VSLAM) solutions, the environment maps acquired by cameras mounted on the mobile robots may be captured in dynamically changing lighting conditions and subject to some other distortions influencing the final quality of the panoramic images representing the robot’s surroundings. Such images may

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Khan, Muhammad Usman, Imran Mehmood, Ming Ronnier Luo, and Muhammad Farhan Mughal. "No-Reference Image Quality Metric for Tone-Mapped Images." Color and Imaging Conference 2019, no. 1 (2019): 252–55. http://dx.doi.org/10.2352/issn.2169-2629.2019.27.45.

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Tone-mapping operators transform high dynamic range (HDR) images into displayable low dynamic range (LDR) images. Image quality evaluation of these LDR images is not possible by comparison with their corresponding high dynamic range images. Hence, a no-reference image quality metric for tone-mapped LDR images is proposed based on the fitting to the present psychophysical results including different visual image quality attributes. Ten images, including HDR natural scenes, were tonemapped using six TMOs. They were used in the assessment and visual attributes were determined to predict the quali

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Bagade, Jayashri V., Kulbir Singh, and Y. H. Dandawate. "No-reference image quality assessment using fusion metric." Multimedia Tools and Applications 79, no. 3-4 (2019): 2109–25. http://dx.doi.org/10.1007/s11042-019-08217-5.

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Ozcinar, Cagri, and Aakanksha Rana. "Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views." Electronic Imaging 2021, no. 9 (2021): 295–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.9.iqsp-295.

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Omnidirectional images (ODIs), also known as 360-degree images, enable viewers to explore all directions of a given 360-degree scene from a fixed point. Designing an immersive imaging system with ODI is challenging as such systems require very large resolution coverage of the entire 360 viewing space to provide an enhanced quality of experience (QoE). Despite remarkable progress on single image super-resolution (SISR) methods with deep-learning techniques, no study for quality assessments of super-resolved ODIs exists to analyze the quality of such SISR techniques. This paper proposes an objec

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Tsai, YuTang, JiaLong Wu, MinChun Huang, KunCheng Tien, and YenWen Fang. "89‐2: An Evaluation Index for See‐Through Image Quality on Transparent MicroLED Displays." SID Symposium Digest of Technical Papers 55, no. 1 (2024): 1234–37. http://dx.doi.org/10.1002/sdtp.17766.

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The objective of this paper was to investigate the quality of see‐through images on transparent MicroLED displays. Transparent displays have gained significant attention in various applications. However, evaluating the quality of see‐through images presented challenges, as there was no universal metric available for assessment. Image analysis methods were employed in this study, and common optical measurement metrics were also compared. The results showcased that CW‐SSIM exhibited higher sensitivity and aligned well with visual observations. This suggests the potential of CW‐SSIM as a universa

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Wu, Di, Fei Yuan, and En Cheng. "Underwater No-Reference Image Quality Assessment for Display Module of ROV." Scientific Programming 2020 (August 28, 2020): 1–15. http://dx.doi.org/10.1155/2020/8856640.

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The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display m

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Jenadeleh, Mohsen, Marius Pedersen, and Dietmar Saupe. "Blind Quality Assessment of Iris Images Acquired in Visible Light for Biometric Recognition." Sensors 20, no. 5 (2020): 1308. http://dx.doi.org/10.3390/s20051308.

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Image quality is a key issue affecting the performance of biometric systems. Ensuring the quality of iris images acquired in unconstrained imaging conditions in visible light poses many challenges to iris recognition systems. Poor-quality iris images increase the false rejection rate and decrease the performance of the systems by quality filtering. Methods that can accurately predict iris image quality can improve the efficiency of quality-control protocols in iris recognition systems. We propose a fast blind/no-reference metric for predicting iris image quality. The proposed metric is based o

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Еремеев, Олег Игоревич, Владимир Васильевич Лукин та Krzysztof Okarma. "КОМБІНОВАНА МЕТРИКА ВІЗУАЛЬНОЇ ЯКОСТІ ЗОБРАЖЕНЬ ДИСТАНЦІЙНОГО ЗОНДУВАННЯ НА ОСНОВІ НЕЙРОННОЇ МЕРЕЖІ". RADIOELECTRONIC AND COMPUTER SYSTEMS, № 4 (27 листопада 2020): 4–15. http://dx.doi.org/10.32620/reks.2020.4.01.

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The wide distribution of images of remote sensing (RS) of the Earth in various application areas makes it important to ensure the high quality of such images, which is important to identify necessary information. The complexity of the systems and the impact of various physical processes cause a significant number of distortions that lead to image corruption and possible loss of information. The use of processing methods that should reduce the impact of such factors requires control of their work, which uses quantitative indicators of visual quality. The article considers the task of creating a

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Garg, Meenu, and Amandeep Verma. "An Enhanced LSDBIQ Algorithm for Full Reference Image Quality Assessment for Multi Distorted Images." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 41. http://dx.doi.org/10.23956/ijarcsse.v7i8.18.

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Image processing is an emerging technology as image is used in various fields like medical and education. Images may corrupt due to the various categories of noises. Image quality reduces because of the image acquisition or transmission. Noise reduction is the main focus to retain the quality of the image. For the removal of this noise, there are various techniques and filters. Before applying further processing on the image, noise should be removed from the image. In this paper we deal with with a practical and effectual IQA model, called LSDBIQ (local standard deviation based image quality).

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Wujie Zhou, Gangyi Jiang, Xiaodong Wang, Mei Yu, Feng Shao, and Zongju Peng. "A Novel Image Quality Assessment Metric based on Image Gradient Vectors." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 18 (2012): 157–64. http://dx.doi.org/10.4156/aiss.vol4.issue18.19.

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Bhateja, Vikrant, Aseem Kalsi, and Aastha Srivastava. "Image similarity metric (ISIM): a reduced reference image quality assessment approach." CSI Transactions on ICT 3, no. 1 (2015): 1–11. http://dx.doi.org/10.1007/s40012-015-0066-8.

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Tian, Jieni, Jiangping Yuan, Hua Li, Danyang Yao, and Guangxue Chen. "Advanced Surface Color Quality Assessment in Paper-Based Full-Color 3D Printing." Materials 14, no. 4 (2021): 736. http://dx.doi.org/10.3390/ma14040736.

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Color 3D printing allows for 3D-printed parts to represent 3D objects more realistically, but its surface color quality evaluation lacks comprehensive objective verification considering printing materials. In this study, a unique test model was designed and printed using eco-friendly and vivid paper-based full-color 3D printing as an example. By measuring the chromaticity, roughness, glossiness, and whiteness properties of 3D-printed surfaces and by acquiring images of their main viewing surfaces, this work skillfully explores the correlation between the color representation of a paper-based 3

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Siniukov, Maksim Vladimirovich, Dmitriy Leonidovich Kulikov, Dmitriy Sergeevich Vatolin, and Vladimir Alexandrovich Galaktionov. "Applicability limitations of differentiable full-reference image quality metrics." Keldysh Institute Preprints, no. 86 (2022): 1–15. http://dx.doi.org/10.20948/prepr-2022-86.

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Subjective image-quality measurement plays a critical role in the development of image-processing applications. The purpose of a visual-quality metric is to approximate the results of subjective assessment. In this regard, more and more metrics are under development, but little research has considered their limitations. This paper addresses that deficiency: we show how image preprocessing before compression can artificially increase the quality scores provided by the popular metrics DISTS, LPIPS, HaarPSI, and VIF as well as how these scores are inconsistent with subjective-quality scores. We p

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Mansoor, Rafia, Muhammad Shahid Farid, Muhammad Hassan Khan, and Asma Maqsood. "A Layered Approach for Quality Assessment of DIBR-Synthesized Images." Wireless Communications and Mobile Computing 2021 (November 13, 2021): 1–14. http://dx.doi.org/10.1155/2021/8377936.

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Multiview video plus depth (MVD) is a popular video format that supports three-dimensional television (3DTV) and free viewpoint television (FTV). 3DTV and FTV provide depth sensation to the viewer by presenting two views of the same scene but with slightly different angles. In MVD, few views are captured, and each view has the color image and the corresponding depth map which is used in depth image-based rendering (DIBR) to generate views at novel viewpoints. The DIBR can introduce various artifacts in the synthesized view resulting in poor quality. Therefore, evaluating the quality of the syn

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Dziembowski, Adrian, Weronika Nowak, and Jakub Stankowski. "IV-SSIM—The Structural Similarity Metric for Immersive Video." Applied Sciences 14, no. 16 (2024): 7090. http://dx.doi.org/10.3390/app14167090.

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In this paper, we present a new objective quality metric designed for immersive video applications—IV-SSIM. The proposed IV-SSIM metric is an evolution of our previous work—IV-PSNR (immersive video peak signal-to-noise ratio)—which became a commonly used metric in research and ISO/IEC MPEG standardization activities on immersive video. IV-SSIM combines the advantages of IV-PSNR and metrics based on the structural similarity of images, being able to properly mimic the subjective quality perception of immersive video with its characteristic distortions induced by the reprojection of pixels betwe

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Ni, Jun, Zi Yin Li, and Hua Cai Chen. "No-Reference Image Quality Assessment Based on Visual Perception." Advanced Engineering Forum 1 (September 2011): 325–29. http://dx.doi.org/10.4028/www.scientific.net/aef.1.325.

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No-reference image quality assessment is an important issue for video compression and communication. This work presents a no-reference objective image/video sharpness method based on visual perception metric (VPM). The algorithm gets image typical edge and edge width firstly, and then gets gray contrast of typical edge region, finally utilizes these factors to integrate a probability summation assessment model. The proposed metric is able to predict the amount of sharpness in image with different content. Experimental results show that this method is consistent with subjective assessment of hu

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Alvi, Hafiz Muhammad Usama Hassan, Muhammad Shahid Farid, Muhammad Hassan Khan, and Marcin Grzegorzek. "Quality Assessment of 3D Synthesized Images Based on Textural and Structural Distortion Estimation." Applied Sciences 11, no. 6 (2021): 2666. http://dx.doi.org/10.3390/app11062666.

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Emerging 3D-related technologies such as augmented reality, virtual reality, mixed reality, and stereoscopy have gained remarkable growth due to their numerous applications in the entertainment, gaming, and electromedical industries. In particular, the 3D television (3DTV) and free-viewpoint television (FTV) enhance viewers’ television experience by providing immersion. They need an infinite number of views to provide a full parallax to the viewer, which is not practical due to various financial and technological constraints. Therefore, novel 3D views are generated from a set of available view

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Jin, Xin, Mei Yu, Shanshan Liu, Yang Song, and Gangyi Jiang. "New image quality assessment metric based on distortion classification." International Journal of Information and Communication Technology 11, no. 2 (2017): 243. http://dx.doi.org/10.1504/ijict.2017.086251.

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Jiang, Gangyi, Yang Song, Mei Yu, Shanshan Liu, and Xin Jin. "New image quality assessment metric based on distortion classification." International Journal of Information and Communication Technology 11, no. 2 (2017): 243. http://dx.doi.org/10.1504/ijict.2017.10007026.

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Yu, Qian, Wei Dong, and Chang N. Zhang. "RCBM: a rough content-based image quality assessment metric." International Journal of Granular Computing, Rough Sets and Intelligent Systems 3, no. 1 (2013): 44. http://dx.doi.org/10.1504/ijgcrsis.2013.054126.

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PhiCong, Huy, Stuart Perry, Eva Cheng, and Xiem HoangVan. "Objective Quality Assessment Metrics for Light Field Image Based on Textural Features." Electronics 11, no. 5 (2022): 759. http://dx.doi.org/10.3390/electronics11050759.

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Light Field (LF) imaging is a plenoptic data collection method enabling a wide variety of image post-processing such as 3D extraction, viewpoint change and digital refocusing. Moreover, LF provides the capability to capture rich information about a scene, e.g., texture, geometric information, etc. Therefore, a quality assessment model for LF images is needed and poses significant challenges. Many LF Image Quality Assessment (LF-IQA) metrics have been recently presented based on the unique characteristics of LF images. The state-of-the-art objective assessment metrics have taken into account th

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Narwaria, Manish, and A. Enis Cetin. "Scalable image quality assessment with 2D mel-cepstrumand machine learning approach." Pattern Recognition 45, no. 1 (2011): 299–313. https://doi.org/10.1016/j.patcog.2011.06.023.

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Measurement of image quality is of fundamental importance to numerous image and video processing applications. Objective image quality assessment (IQA) is a two-stage process comprising of the following: (a) extraction of important information and discarding the redundant one, (b) pooling the detected features using appropriate weights. These two stages are not easy to tackle due to the complex nature of the human visual system (HVS). In this paper, we ﬁrst investigate image features based on two-dimensional (2D) mel-cepstrum for the purpose of IQA. It is shown that these features are effectiv

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Sazzad, Z. M. Parvez, Roushain Akhter, J. Baltes, and Y. Horita. "Objective No-Reference Stereoscopic Image Quality Prediction Based on 2D Image Features and Relative Disparity." Advances in Multimedia 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/256130.

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Stereoscopic images are widely used to enhance the viewing experience of three-dimensional (3D) imaging and communication system. In this paper, we propose an image feature and disparity dependent quality evaluation metric, which incorporates human visible system characteristics. We believe perceived distortions and disparity of any stereoscopic image are strongly dependent on local features, such as edge (i.e., nonplane areas of an image) and nonedge (i.e., plane areas of an image) areas within the image. Therefore, a no-reference perceptual quality assessment method is developed for JPEG cod

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Che, Li, and Fengju Kang. "Exploring the efficiency of image metric for assessing the visual quality of 3D mesh model." International Journal of Modeling, Simulation, and Scientific Computing 09, no. 06 (2018): 1850056. http://dx.doi.org/10.1142/s1793962318500563.

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Recent developments in 3D graphics technology have led to extensive processes on 3D meshes (e.g., compression, simplification, transmission and watermarking), these processes unavoidably cause the visual perceptual degradation of the 3D objects. The existing mesh visual quality evaluation metrics either require topology constrain or fail to reflect the perceived visual quality. Meanwhile, for the 3D objects that are observed on 2D screens by the users, it is reasonable to apply image metric to assess the distortion caused by mesh simplification. We attempt to explore the efficiency of image me

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Hanumantharaju, M. C., M. Ravishankar, D. R. Rameshbabu, and V. N. Manjunath Aradhya. "A Novel Full-Reference Color Image Quality Assessment Based on Energy Computation in the Wavelet Domain." Journal of Intelligent Systems 22, no. 2 (2013): 155–77. http://dx.doi.org/10.1515/jisys-2012-0026.

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AbstractThis article presents a novel full-reference (FR) image quality assessment (QA) algorithm by depicting the sub-band characteristics in the wavelet domain. The proposed image quality assessment method is based on energy estimation in the wavelet-transformed image. Image QA is achieved by applying a multilevel wavelet decomposition on both the original and the enhanced image. Next, the wavelet energy (WE) and vector are computed to obtain the percentage of the energy that corresponds to the approximation and the details, respectively. Further, the approximate and detailed energy levels o

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Ma, Shuang, and Jinhe Liu. "Images Inpainting Quality Evaluation Using Structural Features and Visual Saliency." Advances in Multimedia 2024 (May 14, 2024): 1–10. http://dx.doi.org/10.1155/2024/5066916.

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Despite the extensive research on developing robust image inpainting algorithms in recent years, there are almost no objective metrics for the quality assessment of inpainted images currently. Inspired by the feature coherence in the inpainted image and the human visual perception mechanism, this paper proposes an image inpainting quality assessment (IIQA) that takes into account both visual saliency and structural features. First, the quality issues associated with image inpainting are categorized into three aspects: incoherent structure, unreasonable texture, and other results that are incon

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