Blind Image Quality Assessment Algorithms Research Articles

Blind Image Quality Assessment via Multiperspective Consistency

Blind image quality assessment (BIQA) has made significant progress, but it remains a challenging problem due to the wide variation in image content and the diverse nature of distortions. To address these challenges and improve the adaptability of BIQA algorithms to different image contents and distortions, we propose a novel model that incorporates multiperspective consistency. Our approach introduces a multiperspective strategy to extract features from various viewpoints, enabling us to capture more beneficial cues from the image content. To map the extracted features to a scalar score, we employ a content-aware hypernetwork architecture. Additionally, we integrate all perspectives by introducing a consistency supervision strategy, which leverages cues from each perspective and enforces a learning consistency constraint between them. To evaluate the effectiveness of our proposed approach, we conducted extensive experiments on five representative datasets. The results demonstrate that our method outperforms state-of-the-art techniques on both authentic and synthetic distortion image databases. Furthermore, our approach exhibits excellent generalization ability. The source code is publicly available at https://github.com/gn-share/multi-perspective.

PGF-BIQA: Blind image quality assessment via probability multi-grained cascade forest

Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of a digital image without accessing its pristine reference and plays an important role in computer vision and image analysis. In fact, the difference in the subjective perception of image quality by evaluators greatly affected quality scores of the images. Aiming at the problem that the existing models cannot take into account the subjective perception differences in people, a blind image quality assessment algorithm via probability gcForest (PGF-BIQA) is proposed. Specifically, we use five qualitative labels to replace the specific quality score and extract image color and texture features to represent image quality. Unlike the widely used neural network, gcForest is ensemble by decision trees which randomly extract different features of various modes to learn the relationship between label and quality features. Therefore, a probability gcForest is designed, combining the frequency probability model and the gcForest, using probability confidence to represent the multiple decision modes results, and describing the subjective perception differences in evaluators. Next, through quality anchors and probability confidences of different classes, PGF-BIQA achieves image quality assessment. Moreover, this method proposes an image classification method that resolves the problem of an unbalanced number of images of different classes and the calculation process of the algorithm is simple and effective. Extensive experiments demonstrate that the proposed method is highly consistent with human perception and outperforms many state-of-the-art BIQA algorithms.

Deep blind image quality assessment based on multiple instance regression

In recent years, the research of image quality assessment (IQA) based on deep learning, especially convolutional neural network (CNN), has made rapid development. The most widely used way to build a CNN-based IQA model is to divide image into patches and conduct a patch-based training. However, this method has a critical defect that the local ground-truth for each patch is not available. This defect leads to a sub-optimal result. To address this issue, we propose a novel deep blind IQA algorithm under the multiple instance regression (MIR) framework. Specifically, we assume each instance (patch) has a certain probability to be the prime instance of the bag (image), which is responsible for the bag label. Then the global quality score of the bag can be computed by the weighted summation of the local quality scores of the instances, where the weights are the probabilities of the instances to be the prime one. To simplify the training procedure, we propose an EM-like algorithm, called conditional EM algorithm, to train the deep MIR IQA model. Experimental results show that the proposed deep MIR IQA algorithm performs better than the traditional deep blind IQA algorithm. Moreover, the proposed algorithm can be used as a unified framework to improve the performance of any patch-based deep IQA models.

Blind image quality assessment using a combination of statistical features and CNN

Blind Image Quality Assessment (BIQA) has been an enticing research problem in image processing, during the last few decades. In spite of the introduction of several BIQA algorithms, quantifying image quality without the help of a reference image still remains an unsolved problem. We propose a method for BIQA, combining Natural Scene Statistics (NSS) feature and Probabilistic Quality representation by a CNN. A certain number of features are considered for each image. We also propose to increase the NSS feature set alongside with the same CNN architecture and compare its results accordingly. Support Vector Machine (SVM) regression is applied on these features to get a quality score for that particular image. The results obtained by applying the proposed quality score on benchmark datasets, show the effectiveness of the proposed quality metric compared to the state-of-the-art metrics.

Blind Quality Assessment for Cartoon Images

Current blind image quality assessment (BIQA) algorithms are mainly designed for natural images. Unfortunately, cartoon and cartoon-like images are quite different from natural images. Hence, recent BIQA methods are not very robust to cartoon images. In this paper, we propose a specific BIQA algorithm designed for cartoon images, which consists of the following terms. First, a cartoon image is divided into edge areas and nonedge areas via a Tchebichef moment (TM)-based process. Second, a multiorder sharpness statistic term is used to measure the quality of the edges, and a sharpness statistic prior model of high-quality (HQ) cartoon images is built. Finally, a local encoding statistic term is adopted to describe the textural complexity in the nonedge areas, and a texture statistic prior model is also established. The experimental results on the cartoon image datasets demonstrate that the proposed method can accurately evaluate the visual quality of cartoon images and is more suitable for cartoon scenarios than some traditional BIQA algorithms.

Blind image quality assessment based on Benford's law

The goal of blind image quality assessment (IQA) is to predict the quality of an image as perceived by human observers without using a reference image. The authors explore a new approach which predicts the image quality based on the conformity of the first digit distribution (FDD) of natural images in the transform domain with Benford's law (BL). The conformity is measured by the symmetric Kullback-Leibler divergence. They first show that while in the transform domain the FDD of a natural image conforms with BL well, the FDD of a distorted natural image violates this conformity. They then train a non-linear regression model which maps features derived from the FDD to the subjective evaluation score of an image. The non-linear mapping is trained using Gaussian process regression with a rational quadratic kernel. The selection of this particular non-linear regression tool is based on extensive experiments and evaluations of many regression tools. They conduct experiments to test the proposed technique using five databases. Results demonstrated that its performance is competitive with those state-of-the-art blind IQA algorithms. In particular, the overall performance of the proposed technique is among the best in all algorithms tested.

Blind Image Quality Assessment Using Local Consistency Aware Retriever and Uncertainty Aware Evaluator

Blind image quality assessment (BIQA) aims to automatically predict the perceptual quality of a digital image without accessing its pristine reference. Previous studies mainly focus on extracting various quality-relevant image features. By contrast, the explorations on highly efficient learning model are still very limited. Motivated by the fact that it is difficult to approximate a complex and large data set via a global parametric model, we propose a novel local learning method for BIQA to improve quality prediction performance. More specifically, we search for the perceptually similar neighbors of a test image to serve as its unique training set. Unlike the widely used ${k}$ nearest neighbors principle, which only measures the similarity between the testing and training samples, the local consistency of the selected training data is also considered to generate smoother sample space. The image quality is estimated via a sparse Gaussian process. As an additional benefit, the uncertainty of the predicted score is jointly inferred, which can subsequently drive more robust perceptual image processing applications, such as deblocking investigated in this paper. Extensive experiments demonstrate that the proposed learning model leads to consistent quality prediction improvements over many state-of-the-art BIQA algorithms.

A Multi-Scale Learning Local Phase and Amplitude Blind Image Quality Assessment for Multiply Distorted Images

In the real application, digital images may undergo the process of acquisition, compression, and transmission, which causes the excess blurring, quantization, and noise. However, the metrics of image quality assessment for multiply distorted images are very limited. In this paper, we propose a new multi-scale learning quality-aware features blind image quality assessment algorithm for multiply distorted images by using both local phase and local amplitude. In the new model, a distorted image is decomposed into three scales by Gabor transform, and its phase congruency image (PCI), phase congruency covariance maximum image (PCCmax), and phase congruency covariance minimum image (PCCmin) are produced. Then, we calculate contrast sensitivity function and gray level-gradient co-occurrence matrix features from distorted image and its PCI, PCCmax, and PCCmin, and mean value of intensity of PCI, PCCmax, PCCmin, and overlapping blocked local amplitude of distorted image. At last, SVR is used to build the approximating function between these features and subjective mean opinion scores. Both local phase and local amplitude features are extracted at multi-scale images, which supply more flexibility than the previous single-scale methods in incorporating the variations of viewing scene. Comparative experiments between our proposed metric and the state-of-the-art full-reference and no-reference metrics are conducted on two newly released multiply distorted image databases (LIVEMD and MDID2013) that demonstrate the effectiveness of our proposed method.

Statistical modeling in the shearlet domain for blind image quality assessment

The state-of-the-art blind image quality assessment (BIQA) metrics usually require a large amount of human scored images to train a regression model used to judge image quality, which makes the results are heavily dependent on the size of training data. In this paper, we present an efficient BIQA algorithm based on shearlet transform without using human scored images. This is mainly based on that the degradation of the image leads to significant variation in the spread discontinuities in all directions. However, shearlet transform has a strong ability to localize distributed discontinuities. The natural scene statistics (NSS) of shearlet coefficients are applicable to indicate the variation of image quality. Experimental results on benchmark databases illustrate that the proposed method has a good consistency with the subjective assessment of human beings.

Blind image quality assessment using statistical independence in the divisive normalization transform domain

We present a general purpose blind image quality assessment (IQA) method using the statistical independence hidden in the joint distributions of divisive normalization transform (DNT) representations for natural images. The DNT simulates the redundancy reduction process of the human visual system and has good statistical independence for natural undistorted images; meanwhile, this statistical independence changes as the images suffer from distortion. Inspired by this, we investigate the changes in statistical independence between neighboring DNT outputs across the space and scale for distorted images and propose an independence uncertainty index as a blind IQA (BIQA) feature to measure the image changes. The extracted features are then fed into a regression model to predict the image quality. The proposed BIQA metric is called statistical independence (STAIND). We evaluated STAIND on five public databases: LIVE, CSIQ, TID2013, IRCCyN/IVC Art IQA, and intentionally blurred background images. The performances are relatively high for both single- and cross-database experiments. When compared with the state-of-the-art BIQA algorithms, as well as representative full-reference IQA metrics, such as SSIM, STAIND shows fairly good performance in terms of quality prediction accuracy, stability, robustness, and computational costs.

Massive Online Crowdsourced Study of Subjective and Objective Picture Quality.

Most publicly available image quality databases have been created under highly controlled conditions by introducing graded simulated distortions onto high-quality photographs. However, images captured using typical real-world mobile camera devices are usually afflicted by complex mixtures of multiple distortions, which are not necessarily well-modeled by the synthetic distortions found in existing databases. The originators of existing legacy databases usually conducted human psychometric studies to obtain statistically meaningful sets of human opinion scores on images in a stringently controlled visual environment, resulting in small data collections relative to other kinds of image analysis databases. Toward overcoming these limitations, we designed and created a new database that we call the LIVE In the Wild Image Quality Challenge Database, which contains widely diverse authentic image distortions on a large number of images captured using a representative variety of modern mobile devices. We also designed and implemented a new online crowdsourcing system, which we have used to conduct a very large-scale, multi-month image quality assessment (IQA) subjective study. Our database consists of over 350 000 opinion scores on 1162 images evaluated by over 8100 unique human observers. Despite the lack of control over the experimental environments of the numerous study participants, we demonstrate excellent internal consistency of the subjective data set. We also evaluate several top-performing blind IQA algorithms on it and present insights on how the mixtures of distortions challenge both end users as well as automatic perceptual quality prediction models. The new database is available for public use at http://live.ece.utexas.edu/research/ChallengeDB/index.html.

Learning to rank for blind image quality assessment.

Blind image quality assessment (BIQA) aims to predict perceptual image quality scores without access to reference images. State-of-the-art BIQA methods typically require subjects to score a large number of images to train a robust model. However, subjective quality scores are imprecise, biased, and inconsistent, and it is challenging to obtain a large-scale database, or to extend existing databases, because of the inconvenience of collecting images, training the subjects, conducting subjective experiments, and realigning human quality evaluations. To combat these limitations, this paper explores and exploits preference image pairs (PIPs) such as the quality of image Ia is better than that of image Ib for training a robust BIQA model. The preference label, representing the relative quality of two images, is generally precise and consistent, and is not sensitive to image content, distortion type, or subject identity; such PIPs can be generated at a very low cost. The proposed BIQA method is one of learning to rank. We first formulate the problem of learning the mapping from the image features to the preference label as one of classification. In particular, we investigate the utilization of a multiple kernel learning algorithm based on group lasso to provide a solution. A simple but effective strategy to estimate perceptual image quality scores is then presented. Experiments show that the proposed BIQA method is highly effective and achieves a performance comparable with that of state-of-the-art BIQA algorithms. Moreover, the proposed method can be easily extended to new distortion categories.

Totally Blind Image Quality Assessment Algorithm Based on Weibull Statistics of Natural Scenes

Totally Blind Image Quality Assessment Algorithm Based on Weibull Statistics of Natural Scenes

Fully Blind Image Quality Assessment Algorithm

Fully Blind Image Quality Assessment Algorithm

Universal blind image quality assessment metrics via natural scene statistics and multiple kernel learning.

Universal blind image quality assessment (IQA) metrics that can work for various distortions are of great importance for image processing systems, because neither ground truths are available nor the distortion types are aware all the time in practice. Existing state-of-the-art universal blind IQA algorithms are developed based on natural scene statistics (NSS). Although NSS-based metrics obtained promising performance, they have some limitations: 1) they use either the Gaussian scale mixture model or generalized Gaussian density to predict the nonGaussian marginal distribution of wavelet, Gabor, or discrete cosine transform coefficients. The prediction error makes the extracted features unable to reflect the change in nonGaussianity (NG) accurately. The existing algorithms use the joint statistical model and structural similarity to model the local dependency (LD). Although this LD essentially encodes the information redundancy in natural images, these models do not use information divergence to measure the LD. Although the exponential decay characteristic (EDC) represents the property of natural images that large/small wavelet coefficient magnitudes tend to be persistent across scales, which is highly correlated with image degradations, it has not been applied to the universal blind IQA metrics; and 2) all the universal blind IQA metrics use the same similarity measure for different features for learning the universal blind IQA metrics, though these features have different properties. To address the aforementioned problems, we propose to construct new universal blind quality indicators using all the three types of NSS, i.e., the NG, LD, and EDC, and incorporating the heterogeneous property of multiple kernel learning (MKL). By analyzing how different distortions affect these statistical properties, we present two universal blind quality assessment models, NSS global scheme and NSS two-step scheme. In the proposed metrics: 1) we exploit the NG of natural images using the original marginal distribution of wavelet coefficients; 2) we measure correlations between wavelet coefficients using mutual information defined in information theory; 3) we use features of EDC in universal blind image quality prediction directly; and 4) we introduce MKL to measure the similarity of different features using different kernels. Thorough experimental results on the Laboratory for Image and Video Engineering database II and the Tampere Image Database2008 demonstrate that both metrics are in remarkably high consistency with the human perception, and overwhelm representative universal blind algorithms as well as some standard full reference quality indexes for various types of distortions.