Objective image quality assessment deals with the prediction of digital images' perceptual quality. No-reference image quality assessment predicts the quality of a given input image without any knowledge or information about its pristine (distortion free) counterpart. Machine learning algorithms are heavily used in no-reference image quality assessment because it is very complicated to model the human visual system's quality perception. Moreover, no-reference image quality assessment algorithms are evaluated on publicly available benchmark databases. These databases contain images with their corresponding quality scores. In this study, we evaluate several machine learning based NR-IQA methods and one opinion unaware method on databases consisting of authentic distortions. Specifically, LIVE In the Wild and KonIQ-10k databases were applied to evaluate the state-of-the-art. For machine learning based methods, appx. 80% were used for training and the remaining 20% were used for testing. Furthermore, average PLCC, SROCC, and KROCC values were reported over 100 random train-test splits. The statistics of PLCC, SROCC, and KROCC values were also published using boxplots. Our evaluation results may be helpful to obtain a clear understanding about the status of state-of-the-art no-reference image quality assessment methods.
AIGCIQA2023: A Large-scale Image Quality Assessment Database for AI Generated Images: from the Perspectives of Quality, Authenticity and Correspondence
Shanghai Jiao Tong University（上海交通大学）；Tianjin University（天津大学）；Shanghai Second Polytechnic University（上海第二工业大学）
University of Electronic Science and Technology of China（电子科技大学）
Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understanding and describing the visual contents. Recent researches have devoted great efforts on the task of rain removal for improving the image visibility. However, there is very few exploration about the quality assessment of de-rained image, even it is crucial for accurately measuring the performance of various de-raining algorithms. In this paper, we first create a de-raining quality assessment (DQA) database that collects…
Wuhan University（武汉大学）；New York University（纽约大学）；University of Waterloo（滑铁卢大学）
We propose a deep bilinear model for blind image quality assessment (BIQA) that handles both synthetic and authentic distortions. Our model consists of two convolutional neural networks (CNN), each of which specializes in one distortion scenario. For synthetic distortions, we pre-train a CNN to classify image distortion type and level, where we enjoy large-scale training data. For authentic distortions, we adopt a pre-trained CNN for image classification. The features from the two CNNs are pooled bilinearly into a unified representation for final quality prediction. We then fine-tune the entir…
This short paper presents a perspective plan to build a null reference image quality assessment. Its main goal is to deliver both the objective score and the distortion map for a given distorted image without the knowledge of its reference image.
Over the years, various algorithms were developed, attempting to imitate the Human Visual System (HVS), and evaluate the perceptual image quality. However, for certain image distortions, the functionality of the HVS continues to be an enigma, and echoing its behavior remains a challenge (especially for ill-defined distortions). In this paper, we learn to compare the image quality of two registered images, with respect to a chosen distortion. Our method takes advantage of the fact that at times, simulating image distortion and later evaluating its relative image quality, is easier than assessin…
University of Alberta（阿尔伯塔大学）；CNRS（法国国家科学研究中心）；CentraleSupelec（中央理工-高等电力学院）；Université Paris-Sud（巴黎第十一大学）
Image Quality Assessment algorithms predict a quality score for a pristine or distorted input image, such that it correlates with human opinion. Traditional methods required a non-distorted "reference" version of the input image to compare with, in order to predict this score. However, recent "No-reference" methods circumvent this requirement by modelling the distribution of clean image features, thereby making them more suitable for practical use. However, majority of such methods either use hand-crafted features or require training on human opinion scores (supervised learning), which are dif…
Xidian University（西安电子科技大学）；China University of Mining and Technology（中国矿业大学）
Recently, increasing interest has been drawn in exploiting deep convolutional neural networks (DCNNs) for no-reference image quality assessment (NR-IQA). Despite of the notable success achieved, there is a broad consensus that training DCNNs heavily relies on massive annotated data. Unfortunately, IQA is a typical small sample problem. Therefore, most of the existing DCNN-based IQA metrics operate based on pre-trained networks. However, these pre-trained networks are not designed for IQA task, leading to generalization problem when evaluating different types of distortions. With this motivatio…
In this paper, we analyze the statistics of error signals to assess the perceived quality of images. Specifically, we focus on the magnitude spectrum of error images obtained from the difference of reference and distorted images. Analyzing spectral statistics over grayscale images partially models interference in spatial harmonic distortion exhibited by the visual system but it overlooks color information, selective and hierarchical nature of visual system. To overcome these shortcomings, we introduce an image quality assessment algorithm based on the Spectral Understanding of Multi-scale and …
DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality Assessment
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）
In practical media distribution systems, visual content usually undergoes multiple stages of quality degradation along the delivery chain, but the pristine source content is rarely available at most quality monitoring points along the chain to serve as a reference for quality assessment. As a result, full-reference (FR) and reduced-reference (RR) image quality assessment (IQA) methods are generally infeasible. Although no-reference (NR) methods are readily applicable, their performance is often not reliable. On the other hand, intermediate references of degraded quality are often available, e.…
PIGUIQA: A Physical Imaging Guided Perceptual Framework for Underwater Image Quality Assessment
Chongqing Research Institute of Harbin Institute of Technology（哈尔滨工业大学重庆研究所）；Harbin Institute of Technology（哈尔滨工业大学）；Faculty of Computing（计算机学院）；School of Computer Science（计算机科学学院）；University of Macau（澳门大学）；Institute for Infocomm Research（信息与通信研究所以）
Multitask Auxiliary Network for Perceptual Quality Assessment of Non-Uniformly Distorted Omnidirectional Images
School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算与人工智能学院）；Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing（江西省多媒体智能处理重点实验室）；Harvard Medical School, Harvard University（哈佛大学医学院）
Indian Institute of Technology Gandhinagar（印度理工学院甘地纳加尔分校）；Indian Institute of Technology Hyderabad（印度理工学院海得拉巴分校）
The process of rendering high dynamic range (HDR) images to be viewed on conventional displays is called tone mapping. However, tone mapping introduces distortions in the final image which may lead to visual displeasure. To quantify these distortions, we introduce a novel no-reference quality assessment technique for these tone mapped images. This technique is composed of two stages. In the first stage, we employ a convolutional neural network (CNN) to generate quality aware maps (also known as distortion maps) from tone mapped images by training it with the ground truth distortion maps. In th…
Full-reference image quality assessment (FR-IQA) techniques compare a reference and a distorted/test image and predict the perceptual quality of the test image in terms of a scalar value representing an objective score. The evaluation of FR-IQA techniques is carried out by comparing the objective scores from the techniques with the subjective scores (obtained from human observers) provided in the image databases used for the IQA. Hence, we reasonably assume that the goal of a human observer is to rate the distortion present in the test image. The goal oriented tasks are processed by the human …
Blind or no-reference image quality assessment (NR-IQA) is a fundamental, unsolved, and yet challenging problem due to the unavailability of a reference image. It is vital to the streaming and social media industries that impact billions of viewers daily. Although previous NR-IQA methods leveraged different feature extraction approaches, the performance bottleneck still exists. In this paper, we propose a simple and yet effective general-purpose no-reference (NR) image quality assessment (IQA) framework based on multi-task learning. Our model employs distortion types as well as subjective huma…
In subjective full-reference image quality assessment, differences between perceptual image qualities of the reference image and its distorted versions are evaluated, often using degradation category ratings (DCR). However, the DCR has been criticized since differences between rating categories on this ordinal scale might not be perceptually equidistant, and observers may have different understandings of the categories. Pair comparisons (PC) of distorted images, followed by Thurstonian reconstruction of scale values, overcome these problems. In addition, PC is more sensitive than DCR, and it c…
Content-Distortion High-Order Interaction for Blind Image Quality Assessment
College of Applied Technology, Shenzhen University（深圳大学应用技术学院）；College of Big Data and Internet, Shenzhen Technology University（深圳技术大学大数据与互联网学院）；College of Electronic and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；School of Electronics and Information Engineering, Harbin Institute of Technology（哈尔滨工业大学电子与信息工程学院）；Peng Cheng Laboratory（鹏城实验室）
BPCLIP: A Bottom-up Image Quality Assessment from Distortion to Semantics Based on CLIP
Harbin Institute of Technology（哈尔滨工业大学）；Nanjing University of Information Science and Technology（南京信息工程大学）；Sichuan University of Science & Engineering（四川工程职业技术学院）
Perceptual quality assessment of the videos acquired in the wilds is of vital importance for quality assurance of video services. The inaccessibility of reference videos with pristine quality and the complexity of authentic distortions pose great challenges for this kind of blind video quality assessment (BVQA) task. Although model-based transfer learning is an effective and efficient paradigm for the BVQA task, it remains to be a challenge to explore what and how to bridge the domain shifts for better video representation. In this work, we propose to transfer knowledge from image quality asse…
Subjective and Objective Quality Assessment of Non-Uniformly Distorted Omnidirectional Images
School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（江西财经大学计算与人工智能学院）；Harvard Medical School, Harvard University（哈佛大学医学院）
Blind Image Quality Assessment Using Multi-Stream Architecture with Spatial and Channel Attention
Xi’an Eurasia University（西安欧亚学院）；Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences（中国科学院长春光学精密机械与物理研究所）；University of Engineering and Technology Lahore（拉合尔工程技术大学）
Image quality assessment that aims at estimating the subject quality of images, builds models to evaluate the perceptual quality of the image in different applications. Based on the fact that the human visual system (HVS) is highly sensitive to structural information, the edge information extraction is widely applied in different IQA metrics. According to previous studies, the image gradient magnitude (GM) and the Laplacian of Gaussian (LOG) operator are two efficient structural features in IQA tasks. However, most of the IQA metrics achieve good performance only when the distorted image is to…
JPEG AIC2026: A large-scale dataset for fine-grained assessment of image coding
University of Konstanz（康斯坦茨大学）；Cloudinary（Cloudinary公司）；IST-IT（IST-IT机构）；Fraunhofer IIS（弗劳恩霍夫应用固体物理研究所）；Huawei（华为公司）；Takushoku University（拓殖大学）；IT-UBI（IT-UBI机构）；EPFL（洛桑联邦理工学院）
Collaborative Auto-encoding for Blind Image Quality Assessment
Shenzhen University（深圳大学）；Peng Cheng Laboratory（鹏城实验室）；Shenzhen Institute for Artificial Intelligence and Robotics for Society（深圳人工智能与机器人研究院）；University of Nottingham（诺丁汉大学）
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual quality of images in accordance with human subjective perception. Unfortunately, existing NR-IQA methods are far from meeting the needs of predicting accurate quality scores on GAN-based distortion images. To this end, we propose Multi-dimension Attention Network for no-reference Image Quality Assessment (MANIQA) to improve the performance on GAN-based distortion. We firstly extract features via ViT, then to strengthen global and local interactions, we propose the Transposed Attention Block (TAB) and the Scale Swin Tr…
Bridging Single Distortion Artifacts and Multifactorial Clinical Quality: Few-shot Biparametric MRI Quality Assessment via Distortion-trained Prototypical Networks
UCL Hawkes Institute（UCL Hawkes研究所）；Department of Medical Physics and Biomedical Engineering（医学物理与生物医学工程系）；University College London（伦敦大学学院）；Division of Surgery and Interventional Science（外科与介入科学分会）；Centre for Medical Imaging（医学成像中心）；British Urology Researchers in Surgical Training (BURST)（英国泌尿外科手术培训研究人员（BURST））；Department of Radiology（放射科）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院国家健康服务信托基金）；Centre of Medical Imaging, Division of Medicine（医学成像中心，医学分会）；Centre for Medical Image Computing（医学图像计算中心）；Department of Computer Science（计算机科学系）；Department of Urology（泌尿科）
University of Mohammed V-Agdal（穆罕默德五世大学阿格达勒分校）；University of Burgundy（勃艮第大学）
Color distortion can introduce a significant damage in visual quality perception, however, most of existing reduced-reference quality measures are designed for grayscale images. In this paper, we consider a basic extension of well-known image-statistics based quality assessment measures to color images. In order to evaluate the impact of color information on the measures efficiency, two color spaces are investigated: RGB and CIELAB. Results of an extensive evaluation using TID 2013 benchmark demonstrates that significant improvement can be achieved for a great number of distortion type when th…
Shanghai Jiao Tong University（上海交通大学）；City University of Hong Kong（香港城市大学）
Performance of blind image quality assessment (BIQA) models has been significantly boosted by end-to-end optimization of feature engineering and quality regression. Nevertheless, due to the distributional shift between images simulated in the laboratory and captured in the wild, models trained on databases with synthetic distortions remain particularly weak at handling realistic distortions (and vice versa). To confront the cross-distortion-scenario challenge, we develop a \textit{unified} BIQA model and an approach of training it for both synthetic and realistic distortions. We first sample p…
The problem of stereoscopic image quality assessment, which finds applications in 3D visual content delivery such as 3DTV, is investigated in this work. Specifically, we propose a new ParaBoost (parallel-boosting) stereoscopic image quality assessment (PBSIQA) system. The system consists of two stages. In the first stage, various distortions are classified into a few types, and individual quality scorers targeting at a specific distortion type are developed. These scorers offer complementary performance in face of a database consisting of heterogeneous distortion types. In the second stage, sc…
Q-Doc: Benchmarking Document Image Quality Assessment Capabilities in Multi-modal Large Language Models
South China Normal University（华南师范大学）；Nanyang Technological University（南洋理工大学）；City University of Hong Kong（香港城市大学）；Shenzhen Academy of Inspection and Quarantine（深圳检验检疫局）；Shenzhen University（深圳大学）
Vision Language Modeling of Content, Distortion and Appearance for Image Quality Assessment
College of Electronic and Information Engineering, Shenzhen University（深圳大学电子与信息工程学院）；School of Computer Science, The University of Nottingham Ningbo China（宁波诺丁汉大学计算机学院）；School of Computer Science, University of Nottingham（诺丁汉大学计算机学院）
University of Science and Technology of China（中国科学技术大学）
Image compression has raised widespread interest recently due to its significant importance for multimedia storage and transmission. Meanwhile, a reliable image quality assessment (IQA) for compressed images can not only help to verify the performance of various compression algorithms but also help to guide the compression optimization in turn. In this paper, we design a full-reference image quality assessment metric SwinIQA to measure the perceptual quality of compressed images in a learned Swin distance space. It is known that the compression artifacts are usually non-uniformly distributed w…
University of Texas at Austin（德克萨斯大学奥斯汀分校）；Google Inc.（谷歌公司）
We consider the problem of obtaining image quality representations in a self-supervised manner. We use prediction of distortion type and degree as an auxiliary task to learn features from an unlabeled image dataset containing a mixture of synthetic and realistic distortions. We then train a deep Convolutional Neural Network (CNN) using a contrastive pairwise objective to solve the auxiliary problem. We refer to the proposed training framework and resulting deep IQA model as the CONTRastive Image QUality Evaluator (CONTRIQUE). During evaluation, the CNN weights are frozen and a linear regressor…
