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Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different deep architectures are used for this purpose as they learn features differently. After extensive experiments, we have designed a deep architecture containing two CNN architectures as its sub-units. Moreover, a self-collected image database BIQ2021 is proposed with 12,000 images having natural distortions. The self-collected database is subjectively scored and is used for model training and validation. It is demonstrated that synthetic distortion databases cannot provide generalization beyond the distortion types used in the database and they are not ideal candidates for general-purpose image quality assessment. Moreover, a large-scale database of 18.75 million images with synthetic distortions is used to pretrain the model and then retrain it on benchmark databases for evaluation. Experiments are conducted on six benchmark databases three of which are synthetic distortion databases (LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the performance is demonstrated using perceptually weighted rank correlation to indicatethe perceptual superiority of the proposed approach. Multiple experiments are conducted to validate the generalization performance of the proposed model by training on different subsets of the databases and validating on the test subset of BIQ2021 database.

This paper describes a newly-created image database termed as the NITS-IQA database for image quality assessment (IQA). In spite of recently developed IQA databases, which contain a collection of a huge number of images and type of distortions, there is still a lack of new distortion and use of real natural images taken by the camera. The NITS-IQA database contains total 414 images, including 405 distorted images (nine types of distortion with five levels in each of the distortion type) and nine original images. In this paper, a detailed step by step description of the database development along with the procedure of the subjective test experiment is explained. The subjective test experiment is carried out in order to obtain the individual opinion score of the quality of the images presented before them. The mean opinion score (MOS) is obtained from the individual opinion score. In this paper, the Pearson, Spearman and Kendall rank correlation between a state-of-the-art IQA technique and the MOS are analyzed and presented.

This study introduces a novel Blind Image Quality Assessment (BIQA) approach leveraging a multi-stream spatial and channel attention model. Our method addresses challenges posed by diverse image content and distortions by integrating feature maps from two distinct backbones. Through spatial and channel attention mechanisms, our algorithm prioritizes regions of interest, enhancing its ability to capture crucial image details. Extensive evaluations on four benchmark datasets demonstrate superior performance compared to existing methods, closely aligning with human perceptual assessment. Our approach exhibits exceptional generalization capabilities on both authentic and synthetic distortion databases. Moreover, it demonstrates a distinctive focus on perceptual foreground information, enhancing its practical applicability. Thorough quantitative analyses underscore the algorithm’s superior performance, establishing its dominance over existing methods.

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.

The visual quality evaluation is one of the fundamental challenging problems in image processing. It plays a central role in the shaping, implementation, optimization, and testing of many methods. The existing image quality assessment methods centered mainly on images altered by common distortions while paying little attention to the distortion introduced by color quantization. This happens despite there is a wide range of applications requiring color quantization as a preprocessing step since many color-based tasks are more efficiently accomplished on an image with a reduced number of colors. To fill this gap, at least partially, we carry out a quantitative performance evaluation of nine currently widely-used full-reference image quality assessment measures. The evaluation runs on two publicly available and subjectively rated image quality databases for color quantization degradation by considering their appropriate combinations and subparts. The evaluation results indicate what are the quality measures that have closer performances in terms of their correlation to the subjective human rating and prove that the selected image database significantly impacts the evaluation of the quality measures, although a similar trend on each database is maintained. The detected strong trend similarity, both on individual databases and databases obtained by a proper combination, provides the ability to validate the database combination process and consider the quantitative performance evaluation on each database as an indicator for performance on the other databases. The experimental results are useful to address the choice of appropriate quality measures for color quantization and to improve their future employment.

Taking high quality images at night is a challenging issue for many applications. Therefore, assessing the quality of night-time images (NTIs) is a significant area of research. Since there is no reference image for such images, night-time image quality assessment (NTQA) must be performed blindly. Although the field of blind quality assessment of natural images has gained significant popularity over the past decade, the quality assessment of NTIs usually confront complex distortions such as contrast loss, chroma noise, color desaturation, and detail blur, that have been less investigated. In this paper, a blind night-time image quality evaluation model is proposed by generating innovative quality-aware local feature maps, including detail exposedness, color saturation, sharpness, contrast, and naturalness. In the next step, these maps are compressed and converted into global feature representations using histograms. These feature histograms are used to learn a Gaussian process regression (GPR) quality prediction model. The suggested BIQA approach for night images undergoes a comprehensive evaluation on a standard night image database. The results of the experiments demonstrate the superior prediction performance of the proposed BIQA method for night images compared to other advanced BIQA methods despite its simplicity of implementation and execution speed. Hence, it is readily applicable in real-time scenarios.

As a fundamental research problem, blind image quality assessment (BIQA) has attracted increasing interest in recent years. Although great progress has been made, BIQA still remains a challenge. To better understand the research progress and challenges in this field, we review BIQA methods in this paper. First, we introduce the BIQA problem definition and related methods. Second, we provide a detailed review of the existing BIQA methods in terms of representative hand-crafted features, learning-based features and quality regressors for two-stage methods, as well as one-stage DNN models with various architectures. Moreover, we also present and analyze the performance of competing BIQA methods on six public IQA datasets. Finally, we conclude our paper with possible future research directions based on a performance analysis of the BIQA methods. This review will provide valuable references for researchers interested in the BIQA problem.

In recent years, the High-Dynamic-Range (HDR) image has gained widespread popularity across various domains, such as the security, multimedia, and biomedical fields, owing to its ability to deliver an authentic visual experience. However, the extensive dynamic range and rich detail in HDR images present challenges in assessing their quality. Therefore, current efforts involve constructing subjective databases and proposing objective quality assessment metrics to achieve an efficient HDR Image Quality Assessment (IQA). Recognizing the absence of a systematic overview of these approaches, this paper provides a comprehensive survey of both subjective and objective HDR IQA methods. Specifically, we review 7 subjective HDR IQA databases and 12 objective HDR IQA metrics. In addition, we conduct a statistical analysis of 9 IQA algorithms, incorporating 3 perceptual mapping functions. Our findings highlight two main areas for improvement. Firstly, the size and diversity of HDR IQA subjective databases should be significantly increased, encompassing a broader range of distortion types. Secondly, objective quality assessment algorithms need to identify more generalizable perceptual mapping approaches and feature extraction methods to enhance their robustness and applicability. Furthermore, this paper aims to serve as a valuable resource for researchers by discussing the limitations of current methodologies and potential research directions in the future.

An image when passed through a compression process either gains noise or loses some information, resulting in a degraded image. JPEG is considered to be one of the most commonly used compression standards whose resulting images are found to be subjected to blocking artifacts at low bit rates. There exist a few deblocking algorithms which have been proposed in the literature to reduce the blocking artifacts in compressed images. However, unfortunately these deblocking techniques introduce blur distortion in the images and hence the deblocked images may contain multiple distortions. Existing image quality metrics have limitations in evaluating the quality of deblocked images as they are not designed for multiply distorted images. To overcome this issue, we propose a no-reference quality assessment technique for deblocked images using continuous wavelet transform. We evaluate the proposed technique on DBID database which consists of general deblocked images. Experimental results show that the proposed quality assessment technique outperforms the existing image quality assessment techniques for deblocked images.

The quality of images could be degraded through processing, compression, and transmission. This has created a fundamental need for perceptual quality assessment methods in multimedia services. The use of 3D stereo imaging is rapidly increasing. The quality assessment of stereo images is different from their 2D counterparts. Various algorithms have been devised in this field, mainly based on feature extraction. However, the objective quality scores are not sufficiently correlated with human judgments, or they are not fast enough because of using stereo matching and fusion techniques. In this paper, a fast-efficient algorithm is proposed for blind quality assessment of stereoscopic images. A supervised dictionary learning approach for discriminative sparse representation is applied as an automatic feature discovery framework. Based on this framework, discriminative distortion-specific bases are learned on structural features from stereoscopic images based upon discriminative dictionary learning. The over-complete bases are suitable for sparse representation of samples from the same distortion class. Given these features, an SVR is trained for the no-reference quality assessment of stereoscopic images. The experimental results show that the proposed method has achieved an overall correlation of 97% with subjective scores on common datasets so that its superiority compared to the state-of-the-art methods is up to 2%. Furthermore, our method is computationally efficient and evaluates the quality of stereo images much faster than the competing methods making it qualified for real-time applications.