Due to the scarcity of labeled samples in Image Quality Assessment (IQA) datasets, numerous recent studies have proposed multi-task based strategies, which explore feature information from other tasks or domains to boost the IQA task. Nevertheless, multi-task strategies based No-Reference Image Quality Assessment (NR-IQA) methods encounter several challenges. First, existing methods have not explicitly exploited texture details, which significantly influence the image quality. Second, multi-task methods conventionally integrate features through simple operations such as addition or concatenation, thereby diminishing the network's capacity to accurately represent distorted features. To tackle these challenges, we introduce a novel multi-task NR-IQA framework. Our framework consists of three key components: a high-frequency extraction network, a quality estimation network, and a distortion-aware network. The high-frequency extraction network is designed to guide the model's focus towards high-frequency information, which is highly related to the texture details. Meanwhile, the distortion-aware network extracts distortion-related features to distinguish different distortion types. To effectively integrate features from different tasks, a feature fusion module is developed based on an attention mechanism. Empirical results from five standard IQA databases confirm that our method not only achieves high performance but also exhibits robust generalization ability.
The Loop Game: Quality Assessment and Optimization for Low-Light Image Enhancement
Department of Computer Science, South China Normal University（华南师范大学计算机科学系）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）；College of Computing and Data Science, Nanyang Technological University（南洋理工大学计算与数据科学学院）
City University of Hong Kong（香港城市大学）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
In real-world image enhancement, it is often challenging (if not impossible) to acquire ground-truth data, preventing the adoption of distance metrics for objective quality assessment. As a result, one often resorts to subjective quality assessment, the most straightforward and reliable means of evaluating image enhancement. Conventional subjective testing requires manually pre-selecting a small set of visual examples, which may suffer from three sources of biases: 1) sampling bias due to the extremely sparse distribution of the selected samples in the image space; 2) algorithmic bias due to p…
University of Electronic Science and Technology of China（电子科技大学）
Image quality assessment(IQA) is of increasing importance for image-based applications. Its purpose is to establish a model that can replace humans for accurately evaluating image quality. According to whether the reference image is complete and available, image quality evaluation can be divided into three categories: full-reference(FR), reduced-reference(RR), and non-reference(NR) image quality assessment. Due to the vigorous development of deep learning and the widespread attention of researchers, several non-reference image quality assessment methods based on deep learning have been propose…
Due to the existence of quality degradations introduced in various stages of visual signal acquisition, compression, transmission and display, image quality assessment (IQA) plays a vital role in image-based applications. According to whether the reference image is complete and available, image quality evaluation can be divided into three categories: Full-Reference(FR), Reduced- Reference(RR), and Non- Reference(NR). This article will review the state-of-the-art image quality assessment algorithms.
The main goal of objective image quality assessment is to devise computational, mathematical models which are able to predict perceptual image quality consistently with subjective evaluations. The evaluation of objective image quality assessment algorithms is based on experiments conducted on publicly available benchmark databases. In this study, our goal is to give a comprehensive evaluation about no-reference image quality assessment algorithms, whose original source codes are available online, using the recently published KADID-10k database which is one of the largest available benchmark da…
Significant progress has been made in the past decade for full-reference image quality assessment (FR-IQA). However, new large scale image quality databases have been released for evaluating image quality assessment algorithms. In this study, our goal is to give a comprehensive evaluation of state-of-the-art FR-IQA metrics using the recently published KADID-10k database which is largest available one at the moment. Our evaluation results and the associated discussions is very helpful to obtain a clear understanding about the status of state-of-the-art FR-IQA metrics.
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 c…
Beijing University Posts and Telecommunications（北京邮电大学）
Understanding semantic information is an essential step in knowing what is being learned in both full-reference (FR) and no-reference (NR) image quality assessment (IQA) methods. However, especially for many severely distorted images, even if there is an undistorted image as a reference (FR-IQA), it is difficult to perceive the lost semantic and texture information of distorted images directly. In this paper, we propose a Mask Reference IQA (MR-IQA) method that masks specific patches of a distorted image and supplements missing patches with the reference image patches. In this way, our model o…
University of Science and Technology of China（中国科学技术大学）；Microsoft Research（微软研究院）；University of Rochester（罗切斯特大学）
An important scenario for image quality assessment (IQA) is to evaluate image restoration (IR) algorithms. The state-of-the-art approaches adopt a full-reference paradigm that compares restored images with their corresponding pristine-quality images. However, pristine-quality images are usually unavailable in blind image restoration tasks and real-world scenarios. In this paper, we propose a practical solution named degraded-reference IQA (DR-IQA), which exploits the inputs of IR models, degraded images, as references. Specifically, we extract reference information from degraded images by dist…
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…
Thapar Institute of Engineering and Technology（塔帕尔工程技术学院）；Oxford Brookes University（牛津布鲁克斯大学）
With the inclusion of camera in daily life, an automatic no reference image quality evaluation index is required for automatic classification of images. The present manuscripts proposes a new No Reference Regional Mutual Information based technique for evaluating the quality of an image. We use regional mutual information on subsets of the complete image. Proposed technique is tested on four benchmark natural image databases, and one benchmark synthetic database. A comparative analysis with classical and state-of-art methods indicate superiority of the present technique for high quality images…
City University of Hong Kong（香港城市大学）；University of Missouri-Kansas City（密苏里大学堪萨斯城分校）
In this paper, we propose a no-reference (NR) image quality assessment (IQA) method via feature level pseudo-reference (PR) hallucination. The proposed quality assessment framework is grounded on the prior models of natural image statistical behaviors and rooted in the view that the perceptually meaningful features could be well exploited to characterize the visual quality. Herein, the PR features from the distorted images are learned by a mutual learning scheme with the pristine reference as the supervision, and the discriminative characteristics of PR features are further ensured with the tr…
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.…
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…
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.
A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a no-reference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individua…
The goal of full-reference image quality assessment (FR-IQA) is to predict the quality of an image as perceived by human observers with using its pristine, reference counterpart. In this study, we explore a novel, combined approach which predicts the perceptual quality of a distorted image by compiling a feature vector from convolutional activation maps. More specifically, a reference-distorted image pair is run through a pretrained convolutional neural network and the activation maps are compared with a traditional image similarity metric. Subsequently, the resulted feature vector is mapped o…
University of Science and Technology of China（中国科学技术大学）
In no-reference 360-degree image quality assessment (NR 360IQA), graph convolutional networks (GCNs), which model interactions between viewports through graphs, have achieved impressive performance. However, prevailing GCN-based NR 360IQA methods suffer from three main limitations. First, they only use high-level features of the distorted image to regress the quality score, while the human visual system (HVS) scores the image based on hierarchical features. Second, they simplify complex high-order interactions between viewports in a pairwise fashion through graphs. Third, in the graph construc…
To support the application scenarios where high-resolution (HR) images are urgently needed, various single image super-resolution (SISR) algorithms are developed. However, SISR is an ill-posed inverse problem, which may bring artifacts like texture shift, blur, etc. to the reconstructed images, thus it is necessary to evaluate the quality of super-resolution images (SRIs). Note that most existing image quality assessment (IQA) methods were developed for synthetically distorted images, which may not work for SRIs since their distortions are more diverse and complicated. Therefore, in this paper…
Omnidirectional images, aka 360 images, can deliver immersive and interactive visual experiences. As their popularity has increased dramatically in recent years, evaluating the quality of 360 images has become a problem of interest since it provides insights for capturing, transmitting, and consuming this new media. However, directly adapting quality assessment methods proposed for standard natural images for omnidirectional data poses certain challenges. These models need to deal with very high-resolution data and implicit distortions due to the spherical form of the images. In this study, we…
Low light images suffer from severe noise, low brightness, low contrast, etc. In previous researches, many image enhancement methods have been proposed, but few methods can deal with these problems simultaneously. In this paper, to solve these problems simultaneously, we propose a low light image enhancement method that can combined with supervised learning and previous HSV (Hue, Saturation, Value) or Retinex model based image enhancement methods. First, we analyse the relationship between the HSV color space and the Retinex theory, and show that the V channel (V channel in HSV color space, eq…
Qingdao University（青岛大学）；Ocean University of China（中国海洋大学）
Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of comprehensive human subjective user study with publicly available dataset and reliable objective UIQA metric. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sour…
Fundus Image Quality Assessment and Enhancement: a Systematic Review
Research Institute of Trustworthy Autonomous Systems, SUSTech（南方科技大学可信自主系统研究院）；Department of Computer Science and Engineering, SUSTech（南方科技大学计算机科学与工程系）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Computer School, Beijing Information Science and Technology University（北京信息科技大学计算机学院）；Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（新加坡科技研究局高性能计算研究所）
University of Würzburg（维尔茨堡大学）；Institute of Computer Science（计算机科学研究所）
Generative models for image restoration, enhancement, and generation have significantly improved the quality of the generated images. Surprisingly, these models produce more pleasant images to the human eye than other methods, yet, they may get a lower perceptual quality score using traditional perceptual quality metrics such as PSNR or SSIM. Therefore, it is necessary to develop a quantitative metric to reflect the performance of new algorithms, which should be well-aligned with the person's mean opinion score (MOS). Learning-based approaches for perceptual image quality assessment (IQA) usua…
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…
Harbin Institute of Technology（哈尔滨工业大学）；Dalian Maritime University（大连海事大学）；Peng Cheng Laboratory（鹏城实验室）
Full-reference (FR) image quality assessment (IQA) evaluates the visual quality of a distorted image by measuring its perceptual difference with pristine-quality reference, and has been widely used in low-level vision tasks. Pairwise labeled data with mean opinion score (MOS) are required in training FR-IQA model, but is time-consuming and cumbersome to collect. In contrast, unlabeled data can be easily collected from an image degradation or restoration process, making it encouraging to exploit unlabeled training data to boost FR-IQA performance. Moreover, due to the distribution inconsistency…
In this work, we introduce Gradient Siamese Network (GSN) for image quality assessment. The proposed method is skilled in capturing the gradient features between distorted images and reference images in full-reference image quality assessment(IQA) task. We utilize Central Differential Convolution to obtain both semantic features and detail difference hidden in image pair. Furthermore, spatial attention guides the network to concentrate on regions related to image detail. For the low-level, mid-level and high-level features extracted by the network, we innovatively design a multi-level fusion m…
Self-Reference Deep Adaptive Curve Estimation for Low-Light Image Enhancement
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Maynooth International Engineering College（梅努斯国际工程学院）；National University of Ireland Maynooth（爱尔兰国立梅努斯大学）；Southern University of Science and Technology（南方科技大学）；Northeastern University（东北大学）；NODAR Inc.（NODAR公司）
Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）；University College London（伦敦大学学院）；Ping An Technology Co., Ltd.（平安科技（深圳）有限公司）
Human fingerprints are detailed and nearly unique markers of human identity. Such a unique and stable fingerprint is also left on each acquired image. It can reveal how an image was degraded during the image acquisition procedure and thus is closely related to the quality of an image. In this work, we propose a new no-reference image quality assessment (NR-IQA) approach called domain-aware IQA (DA-IQA), which for the first time introduces the concept of domain fingerprint to the NR-IQA field. The domain fingerprint of an image is learned from image collections of different degradations and the…
No-reference image quality assessment (NR-IQA) is a fundamental yet challenging task in low-level computer vision community. The difficulty is particularly pronounced for the limited information, for which the corresponding reference for comparison is typically absent. Although various feature extraction mechanisms have been leveraged from natural scene statistics to deep neural networks in previous methods, the performance bottleneck still exists. In this work, we propose a hallucination-guided quality regression network to address the issue. We firstly generate a hallucinated reference const…
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…
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…
ZhongAn Information Technology Service Co., Ltd.（众安信息技术服务有限公司）
Document image quality assessment (DIQA) is an important and challenging problem in real applications. In order to predict the quality scores of document images, this paper proposes a novel no-reference DIQA method based on character gradient, where the OCR accuracy is used as a ground-truth quality metric. Character gradient is computed on character patches detected with the maximally stable extremal regions (MSER) based method. Character patches are essentially significant to character recognition and therefore suitable for use in estimating document image quality. Experiments on a benchmark…
This paper presents a high-performance general-purpose no-reference (NR) image quality assessment (IQA) method based on image entropy. The image features are extracted from two domains. In the spatial domain, the mutual information between the color channels and the two-dimensional entropy are calculated. In the frequency domain, the two-dimensional entropy and the mutual information of the filtered sub-band images are computed as the feature set of the input color image. Then, with all the extracted features, the support vector classifier (SVC) for distortion classification and support vector…
Universidade Federal do Espírito Santo（圣埃斯皮里图联邦大学）
This paper uses robust statistics and curvelet transform to learn a general-purpose no-reference (NR) image quality assessment (IQA) model. The new approach, here called M1, competes with the Curvelet Quality Assessment proposed in 2014 (Curvelet2014). The central idea is to use descriptors based on robust statistics to extract features and predict the human opinion about degraded images. To show the consistency of the method the model is tested with 3 different datasets, LIVE IQA, TID2013 and CSIQ. To test evaluation, it is used the Wilcoxon test to verify the statistical significance of resu…
Pierre and Marie Curie University（皮埃尔和玛丽居里大学）；ESPCI Paris, PSL Research University（巴黎高等物理化工学院，巴黎文理研究大学）；National University of Defense Technology（国防科技大学）；Peking University（北京大学）；Sun Yat-Sen University（中山大学）
During the last decades, the number of new full-reference image quality assessment algorithms has been increasing drastically. Yet, despite of the remarkable progress that has been made, the medical ultrasound image similarity measurement remains largely unsolved due to a high level of speckle noise contamination. Potential applications of the ultrasound image similarity measurement seem evident in several aspects. To name a few, ultrasound imaging quality assessment, abnormal function region detection, etc. In this paper, a comparative study was made on full-reference image quality assessment…
University of Science and Technology of China（中国科学技术大学）；Nanjing University of Information Science and Technology（南京信息工程大学）
Light field image (LFI) quality assessment is becoming more and more important, which helps to better guide the acquisition, processing and application of immersive media. However, due to the inherent high dimensional characteristics of LFI, the LFI quality assessment turns into a multi-dimensional problem that requires consideration of the quality degradation in both spatial and angular dimensions. Therefore, we propose a novel Tensor oriented No-reference Light Field image Quality evaluator (Tensor-NLFQ) based on tensor theory. Specifically, since the LFI is regarded as a low-rank 4D tensor,…
Miovision Technologies Inc.（Miovision Technologies 公司）；University of Waterloo（滑铁卢大学）
Image quality assessment (IQA) continues to garner great interest in the research community, particularly given the tremendous rise in consumer video capture and streaming. Despite significant research effort in IQA in the past few decades, the area of no-reference image quality assessment remains a great challenge and is largely unsolved. In this paper, we propose a novel no-reference image quality assessment system called Deep Quality, which leverages the power of deep learning to model the complex relationship between visual content and the perceived quality. Deep Quality consists of a nove…
No-reference image quality assessment (NR-IQA) aims to measure the image quality without reference image. However, contrast distortion has been overlooked in the current research of NR-IQA. In this paper, we propose a very simple but effective metric for predicting quality of contrast-altered images based on the fact that a high-contrast image is often more similar to its contrast enhanced image. Specifically, we first generate an enhanced image through histogram equalization. We then calculate the similarity of the original image and the enhanced one by using structural-similarity index (SSIM…
Beijing University of Posts and Telecommunications（北京邮电大学）
It is suggested that low-light image enhancement realizes one-to-many mapping since we have different definitions of NORMAL-light given application scenarios or users' aesthetic. However, most existing methods ignore subjectivity of the task, and simply produce one result with fixed brightness. This paper proposes a neural network for multi-level low-light image enhancement, which is user-friendly to meet various requirements by selecting different images as brightness reference. Inspired by style transfer, our method decomposes an image into two low-coupling feature components in the latent s…
An innovative way of calculating the von Mises distribution (VMD) of image entropy is introduced in this paper. The VMD's concentration parameter and some fitness parameter that will be later defined, have been analyzed in the experimental part for determining their suitability as a image quality assessment measure in some particular distortions such as Gaussian blur or additive Gaussian noise. To achieve such measure, the local Rényi entropy is calculated in four equally spaced orientations and used to determine the parameters of the von Mises distribution of the image entropy. Considering co…
Fraunhofer Heinrich Hertz Institute (Fraunhofer HHI)（弗劳恩霍夫海因里希·赫兹研究所）；Berlin Institute of Technology（柏林工业大学）
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained end-to-end and comprises ten convolutional layers and five pooling layers for feature extraction, and two fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that: 1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and 2) it allows for joint learning of local quality and local weights, i.e., relative importance of local quality to th…
Computer Vision Center（计算机视觉中心）；University of Florence（佛罗伦萨大学）
We propose a no-reference image quality assessment (NR-IQA) approach that learns from rankings (RankIQA). To address the problem of limited IQA dataset size, we train a Siamese Network to rank images in terms of image quality by using synthetically generated distortions for which relative image quality is known. These ranked image sets can be automatically generated without laborious human labeling. We then use fine-tuning to transfer the knowledge represented in the trained Siamese Network to a traditional CNN that estimates absolute image quality from single images. We demonstrate how our ap…
University Mohammed V-Agdal（穆罕默德五世大学阿格达尔分校）；University Ibn Zohr（伊本·佐赫尔大学）；University of Burgundy（勃艮第大学）
Although color is a fundamental feature of human visual perception, it has been largely unexplored in the reduced-reference (RR) image quality assessment (IQA) schemes. In this paper, we propose a natural scene statistic (NSS) method, which efficiently uses this information. It is based on the statistical deviation between the steerable pyramid coefficients of the reference color image and the degraded one. We propose and analyze the multivariate generalized Gaussian distribution (MGGD) to model the underlying statistics. In order to quantify the degradation, we develop and evaluate two measur…
