No-reference Image Quality Assessment Method Research Articles

Joint Distortion Restoration and Quality Feature Learning for No-reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) methods, inspired by the free energy principle, improve the accuracy of image quality prediction by simulating the human brain’s repair process for distorted images. However, existing methods use separate optimization schemes for distortion restoration and quality prediction, which undermines the accurate mapping of feature representations to quality scores. To address this issue, we propose a joint restoration and quality feature learning NR-IQA (RQFL-IQA) method to jointly tackle distortion image restoration and quality prediction within a unified framework. To accurately establish the quality reconstruction relationship between distorted and restored images, a hybrid loss function based on pixel-wise and structure-wise representations is used to improve the restoration capability of the image restoration network. The proposed RQFL-IQA exploits rich labels, including restored images and quality scores, to enable the model to learn more discriminative features and establish a more accurate mapping from feature representation to quality scores. In addition, to avoid the impact of poor restoration on quality prediction, we propose a module with a cleaning function to reweight the fusion of restored and primitive features to achieve more perceptual consistency in feature fusion. Experimental results on public IQA datasets show that the proposed RQFL-IQA is superior over existing methods.

Transformer-Based No-Reference Image Quality Assessment via Supervised Contrastive Learning

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 a large-scale synthetic dataset by SCL (no image subjective score is required) to extract degradation features of images with various distortion types and levels. To further extract distortion information from images, we propose a backbone network incorporating the Multi-Stream Block (MSB) by combining the CNN inductive bias and Transformer long-term dependence modeling capability. Finally, we propose the Patch Attention Block (PAB) to obtain the final distorted image quality score by fusing the degradation features learned from contrastive learning with the perceptual distortion information extracted by the backbone network. Experimental results on six standard IQA datasets show that SaTQA outperforms the state-of-the-art methods for both synthetic and authentic datasets. Code is available at https://github.com/I2-Multimedia-Lab/SaTQA.

Auxiliary Information Guided Self-attention for Image Quality Assessment

Image quality assessment (IQA) is an important problem in computer vision with many applications. We propose a transformer-based multi-task learning framework for the IQA task. Two subtasks: constructing an auxiliary information error map and completing image quality prediction, are jointly optimized using a shared feature extractor. We use visual transformers (ViT) as a feature extractor for feature extraction and guide ViT to focus on image quality-related features by building auxiliary information error map subtask. In particular, we propose a fusion network that includes a channel focus module. Unlike the fusion methods commonly used in previous IQA methods, we use the fusion network, including the channel attention module, to fuse the auxiliary information error map features with the image features, which facilitates the model to mine the image quality features for more accurate image quality assessment. And by jointly optimizing the two subtasks, ViT focuses more on extracting image quality features and building a more precise mapping from feature representation to quality score. With slight adjustments to the model, our approach can be used in both no-reference (NR) and full-reference (FR) IQA environments. We evaluate the proposed method in multiple IQA databases, showing better performance than state-of-the-art FR and NR IQA methods.

Subjective and Objective Quality Assessment for in-the-Wild Computer Graphics Images

Computer graphics images (CGIs) are artificially generated by means of computer programs and are widely perceived under various scenarios, such as games, streaming media, etc. In practice, the quality of CGIs consistently suffers from poor rendering during production, inevitable compression artifacts during the transmission of multimedia applications, and low aesthetic quality resulting from poor composition and design. However, few works have been dedicated to dealing with the challenge of computer graphics image quality assessment (CGIQA). Most image quality assessment (IQA) metrics are developed for natural scene images (NSIs) and validated on databases consisting of NSIs with synthetic distortions, which are not suitable for in-the-wild CGIs. To bridge the gap between evaluating the quality of NSIs and CGIs, we construct a large-scale in-the-wild CGIQA database consisting of 6,000 CGIs (CGIQA-6k) and carry out the subjective experiment in a well-controlled laboratory environment to obtain the accurate perceptual ratings of the CGIs. Then, we propose an effective deep learning–based no-reference (NR) IQA model by utilizing both distortion and aesthetic quality representation. Experimental results show that the proposed method outperforms all other state-of-the-art NR IQA methods on the constructed CGIQA-6k database and other CGIQA-related databases. The database is released athttps://github.com/zzc-1998/CGIQA6K.

EARNet: Error-Aware Reconstruction Network for no-reference image quality assessment

Deep learning-based no-reference image quality assessment (NRIQA) methods have demonstrated advanced performance. In this paper, a deep learning-based NRIQA method with strong error-aware and content-aware capabilities is proposed, which consists of Error-Aware Reconstruction Network (EARNet) module, Content Feature Extraction Network (CFENet) module, and Subjective Quality Regression Network (SQRNet) module. We first build a database to pre-train EARNet to obtain the ability to extract error features. For content features, CFENet pre-trained on large-scale image classification tasks is adopted to extract. The pre-trained EARNet and CFENet are serially connected with SQRNet so that the features received by SQRNet are both error-aware and content-aware. Extensive experimental results show that the proposed method achieves state-of-the-art performance on many well-known IQA databases. The robustness of the proposed method is verified on the large-scale Waterloo Exploration Database (WED), and its superiority is demonstrated by the group maximum differentiated (gMAD) competition game. Furthermore, we also verify that the proposed EARNet is highly extensible, which can further improve the performance of the existing deep learning-based NRIQA method.

Perceptual Quality Assessment of Enhanced Colonoscopy Images: A Benchmark Dataset and an Objective Method

In colonoscopy, the captured images are usually with low-quality appearance, such as non-uniform illumination, low contrast, etc., due to the specialized imaging environment, which may provide poor visual feedback and bring challenges to subsequent disease analysis. Many low-light image enhancement (LIE) algorithms have recently proposed to improve the perceptual quality. However, how to fairly evaluate the quality of enhanced colonoscopy images (ECIs) generated by different LIE algorithms remains a rarely-mentioned and challenging problem. In this study, we carry out a pioneering investigation on perceptual quality assessment of ECIs. Firstly, considering the lack of specific datasets, we collect 300 low-light images with diverse contents during the real-world colonoscopy and conduct rigorous subjective studies to compare the performance of 8 popular LIE methods, resulting in a benchmark dataset (named ECIQAD) for ECIs. Secondly, in view of the distinctive distortion characteristics of ECIs, we propose an effective no-reference Enhanced Colonoscopy Image Quality (ECIQ) method to automatically evaluate the perceptual quality of ECIs via analysis of brightness, contrast, colorfulness, naturalness, and noise. Extensive experiments on ECIQAD demonstrate the superiority of our proposed ECIQ method over 14 mainstream no-reference image quality assessment methods.

Realistic feature perception for face frontalization with dual-mode face transformation

Despite face frontalization developing rapidly, limited image pairs and low image quality are still obstacles to face frontalization. Although single-view-based face frontalization does not have the constraints of image pairs, it reduces the exploration of unknown data. We alleviate the face distortion problem based on single-view face frontalization from two aspects. One is that we designed a dual-mode face transformation model for model training based on the loss of facial features from different angles. These two training modes cope with the question of face frontalization in different distortions. Specifically, we apply the 3D head model to the data transformation by fitting the 2D image to the 3D model. Rotating the 3D model at the designed angle to realize the face transformation. We judge the distortion extent with Euler Angle and produce the two training modes corresponding to the dual-mode face transformation. On the other hand, we construct a perceptual loss module to preserve the detailed information of the front view. This module combines content and identity loss to generate the photorealistic frontal image. In qualitative and quantitative experiments, we conduct experiments to illustrate the advantages of the proposed model on controlled and field datasets. Further, we apply full-reference and no-reference image quality assessment methods to estimate the quality of images generated by different face frontalization models.

Two No-Reference Image Quality Assessment methods based on Possibilistic Choquet Integral and Entropy: Application to Automatic Fingerprint Identification Systems

Two No-Reference Image Quality Assessment methods based on Possibilistic Choquet Integral and Entropy: Application to Automatic Fingerprint Identification Systems

No-Reference Image Quality Assessment Based on a Multitask Image Restoration Network

When image quality is evaluated, the human visual system (HVS) infers the details in the image through its internal generative mechanism. In this process, the HVS integrates both local and global information about the image, utilizes contextual information to restore the original image information, and compares it with the distorted image information for image quality evaluation. Inspired by this mechanism, a no-reference image quality assessment method is proposed based on a multitask image restoration network. The multitask image restoration network generates a pseudo-reference image as the main task and produces a structural similarity index measure map as an auxiliary task. By mutually promoting the two tasks, a higher-quality pseudo-reference image is generated. In addition, when predicting the image quality score, both the quality restoration features and the difference features between the distorted and reference images are used, thereby fully utilizing the information from the pseudo-reference image. In order to facilitate the model’s ability to extract both global and local features, we introduce a multi-scale feature fusion module. Experimental results demonstrate that the proposed method achieves excellent performance on both synthetically and authentically distorted databases.

Multi-layer and Multi-scale feature aggregation for DIBR-Synthesized image quality assessment

Depth-Image-Based Rendering (DIBR) is one of the main fundamental techniques for generating new viewpoints in 3D video applications such as multi-viewpoint video (MVV), free viewpoint video (FVV) and virtual reality (VR). Due to the imperfections of color images, depth maps or texture restoration techniques, several types of distortions occur in synthesized views. However, most of related works evaluated the quality of DIBR-synthesized views by only detecting a specific type of distortion, such as stretching, black holes, blurring, etc., which were unable to accurately evaluate the quality of DIBR-synthesized views. In this paper, a new no-reference image quality assessment method is proposed to evaluate the quality of DIBR-synthesized images by combining multi-layer and multi-scale features of images. To be specific, the distortions introduced by different stages of virtual viewpoint synthesis are first analyzed, and then multi-layer and multi-scale features are extracted to estimate the degree of texture and structure distortions. As a result, individual quality scores associated with two types of distortions (e.g., structural distortion and texture distortion) are aggregated to an overall image quality. Experimental results on two publicly available DIBR datasets show that the method has better performance than the state-of-the-art models.Index Terms: image quality assessment, DIBR-synthesized image, distortion correction, BIQA.

No-Reference Image Quality Assessment via Multibranch Convolutional Neural Networks

No-reference image quality assessment (NR-IQA) aims to evaluate image quality without using the original reference images. Since the early NR-IQA methods based on distortion types were only applicable to specific distortion scenarios, and lack of practicality, it is challenging to designing a universal NR-IQA method. In this article, a multibranch convolutional neural network (MB-CNN) based NR-IQA method is proposed, which includes a spatial-domain feature extractor, a gradient-domain feature extractor, and a weight mechanism. The spatial-domain feature extractor aims to extract the distortion features from the spatial domain. The gradient-domain feature extractor is used to guide the spatial-domain feature extractor to pay more attention to the distortions of the structure information. Particularly, the spatial-domain feature extractor uses the hierarchical feature merge module to realize multiscale feature representation, and the gradient-domain feature extractor uses pyramidal convolution to extract the multiscale structure information of the distorted image. In addition, a position vector is proposed to build the weight mechanism by considering the position relationships between patches and its entire image for improving the final prediction performance. We conduct the experiments on five representative databases: LIVE, TID2013, CSIQ, LIVE MD and Waterloo Exploration Database, and the experimental results show that the proposed NR-IQA method achieves the state-of-the-art performance, which demonstrate the effectiveness of our proposed NR-IQA method. The code ofthe proposed MB-CNN will be released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUIST-Videocoding/MB-CNN</uri> .

A Multiscale Approach to Deep Blind Image Quality Assessment.

Faithful measurement of perceptual quality is of significant importance to various multimedia applications. By fully utilizing reference images, full-reference image quality assessment (FR-IQA) methods usually achieves better prediction performance. On the other hand, no-reference image quality assessment (NR-IQA), also known as blind image quality assessment (BIQA), which does not consider the reference image, makes it a challenging but important task. Previous NR-IQA methods have focused on spatial measures at the expense of information in the available frequency bands. In this paper, we present a multiscale deep blind image quality assessment method (BIQA, M.D.) with spatial optimal-scale filtering analysis. Motivated by the multi-channel behavior of the human visual system and contrast sensitivity function, we decompose an image into a number of spatial frequency bands by multiscale filtering and extract features for mapping an image to its subjective quality score by applying convolutional neural network. Experimental results show that BIQA, M.D. compares well with existing NR-IQA methods and generalizes well across datasets.

No-reference perceptual CT image quality assessment based on a self-supervised learning framework

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 images or radiologists’ opinions in a real-time clinical environment makes IQA challenging. Thus, no-reference IQA (NR-IQA) is more desirable in clinical settings than full-reference IQA (FR-IQA). Leveraging an innovative self-supervised training strategy for object detection models by detecting virtually inserted objects with geometrically simple forms, we propose a novel NR-IQA method, named deep detector IQA (D2IQA), that can automatically calculate the quantitative quality of CT images. Extensive experimental evaluations on clinical and anthropomorphic phantom CT images demonstrate that our D2IQA is capable of robustly computing perceptual image quality as it varies according to relative dose levels. Moreover, when considering the correlation between the evaluation results of IQA metrics and radiologists’ quality scores, our D2IQA is marginally superior to other NR-IQA metrics and even shows performance competitive with FR-IQA metrics.

No-reference image quality assessment with multi-scale weighted residuals and channel attention mechanism

With the rapid development of deep learning, no-reference image quality assessment (NR-IQA) based on convolutional neural network (CNN) plays an important role in image processing. Currently, most CNN-based NR-IQA methods focus primarily on the global features of images while ignoring detail-rich local features and channel dependencies. In fact, there are subtle differences in detail between distorted and reference images, as well as differences in the contribution of different channels to IQA. Furthermore, multi-scale feature extraction can be used to fuse the detailed information from images with different resolutions, and the combination of global and local features is critical in extracting image features. As a result, in this paper, a multi-scale residual CNN with an attention mechanism (MsRCANet) is proposed for NR-IQA. Specifically, a multi-scale residual block is first used to extract features from distorted images. Then, the residual learning with active weighted mapping strategy and channel attention mechanism is used to further process image features to obtain more abundant information. Finally, the fusion strategy and full connection layer are used to evaluate image quality. The experimental results on four synthetic databases and three in-the-wild IQA databases, as well as cross-database validation results, show that the proposed method has good generalization ability and can be compared with the most advanced methods.

A Human Visual System Inspired No-Reference Image Quality Assessment Method Based on Local Feature Descriptors.

Objective quality assessment of natural images plays a key role in many fields related to imaging and sensor technology. Thus, this paper intends to introduce an innovative quality-aware feature extraction method for no-reference image quality assessment (NR-IQA). To be more specific, a various sequence of HVS inspired filters were applied to the color channels of an input image to enhance those statistical regularities in the image to which the human visual system is sensitive. From the obtained feature maps, the statistics of a wide range of local feature descriptors were extracted to compile quality-aware features since they treat images from the human visual system’s point of view. To prove the efficiency of the proposed method, it was compared to 16 state-of-the-art NR-IQA techniques on five large benchmark databases, i.e., CLIVE, KonIQ-10k, SPAQ, TID2013, and KADID-10k. It was demonstrated that the proposed method is superior to the state-of-the-art in terms of three different performance indices.

Dual-Channel Multi-Task CNN for No-Reference Screen Content Image Quality Assessment

Nowadays the problem of image quality assessment (IQA) for screen content images (SCIs) has become a research hotspot as they are ubiquitous in multimedia applications. Although the quality assessment of natural images (NIs) has been continuously developed in the past few decades, few NI-oriented IQA methods can be directly applied on SCIs due to different visual characteristics between them. In this paper, we present a no-reference quality prediction approach considering the content information of SCIs, which is based on dual-channel multi-task convolutional neural network. First, we segment a SCI into small patches and classify them as the textual patches and the pictorial patches. Then, we devise a novel dual-channel convolutional neural network (CNN) to predict the quality of textual patches and pictorial patches. Finally, we propose an effective adaptive weighting strategy for quality score aggregation. The proposed CNN is built on an end-to-end multi-task learning framework, which assists the SCI quality prediction task through the histogram of oriented gradient (HOG) feature prediction task to learn a better mapping between the input patch and its quality score. The adaptive weighting strategy further improves the representation ability of each SCI patch. Experimental results on two largest SCI-oriented databases demonstrate that the proposed method outperforms most of the state-of-the-art no-reference IQA methods and the full-reference IQA methods.

A Brief Survey on No-Reference Image Quality Assessment Methods for Magnetic Resonance Images.

No-reference image quality assessment (NR-IQA) methods automatically and objectively predict the perceptual quality of images without access to a reference image. Therefore, due to the lack of pristine images in most medical image acquisition systems, they play a major role in supporting the examination of resulting images and may affect subsequent treatment. Their usage is particularly important in magnetic resonance imaging (MRI) characterized by long acquisition times and a variety of factors that influence the quality of images. In this work, a survey covering recently introduced NR-IQA methods for the assessment of MR images is presented. First, typical distortions are reviewed and then popular NR methods are characterized, taking into account the way in which they describe MR images and create quality models for prediction. The survey also includes protocols used to evaluate the methods and popular benchmark databases. Finally, emerging challenges are outlined along with an indication of the trends towards creating accurate image prediction models.

No-reference image quality assessment for confocal endoscopy images with perceptual local descriptor

.Significance: Confocal endoscopy images often suffer distortions, resulting in image quality degradation and information loss, increasing the difficulty of diagnosis and even leading to misdiagnosis. It is important to assess image quality and filter images with low diagnostic value before diagnosis.Aim: We propose a no-reference image quality assessment (IQA) method for confocal endoscopy images based on Weber’s law and local descriptors. The proposed method can detect the severity of image degradation by capturing the perceptual structure of an image.Approach: We created a new dataset of 642 confocal endoscopy images to validate the performance of the proposed method. We then conducted extensive experiments to compare the accuracy and speed of the proposed method with other state-of-the-art IQA methods.Results: Experimental results demonstrate that the proposed method achieved an SROCC of 0.85 and outperformed other IQA methods.Conclusions: Given its high consistency in subjective quality assessment, the proposed method can screen high-quality images in practical applications and contribute to diagnosis.

No-Reference Image Quality Assessment Based on Image Multi-Scale Contour Prediction

Accurately assessing image quality is a challenging task, especially without a reference image. Currently, most of the no-reference image quality assessment methods still require reference images in the training stage, but reference images are usually not available in real scenes. In this paper, we proposed a model named MSIQA inspired by biological vision and a convolution neural network (CNN), which does not require reference images in the training and testing phases. The model contains two modules, a multi-scale contour prediction network that simulates the contour response of the human optic nerve to images at different distances, and a central attention peripheral inhibition module inspired by the receptive field mechanism of retinal ganglion cells. There are two steps in the training stage. In the first step, the multi-scale contour prediction network learns to predict the contour features of images in different scales, and in the second step, the model combines the central attention peripheral inhibition module to learn to predict the quality score of the image. In the experiments, our method has achieved excellent performance. The Pearson linear correlation coefficient of the MSIQA model test on the LIVE database reached 0.988.

No-reference image quality assessment for dehazed images

Traditional image quality prediction methods require the pristine image as a reference, such as structural similarity. However, it is difficult to provide a haze-free image as a reference when predicting the quality of the dehazed image. Therefore, it is necessary to use a no-reference image quality assessment (NR-IQA) method. In addition, most NR-IQA methods are based on known distortion type, using a large number of subjective opinion scores and images with the same distortion to train the model. We developed an innovative NR-IQA specifically for dehazed images without such prior knowledge. Since most images will undergo color distortion and blur after dehazing, it is proposed to combine color and sharpness for evaluation. The quality of the image is evaluated on the HSI color space, where the H and S channels are utilized to evaluate color, and the I channel to sharpness. Experimental results show that the performance of the proposed metric is better than other existing evaluation methods for dehazed images.