scholarly journals No-Reference Image Quality Assessment with Local Gradient Orientations

Symmetry ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 95 ◽  
Author(s):  
Mariusz Oszust
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Support Vector ◽  
Human Observer ◽  
Learning Approaches ◽  
Benchmark Datasets ◽  
Highly Correlated ◽  
Local Gradient

Image processing methods often introduce distortions, which affect the way an image is subjectively perceived by a human observer. To avoid inconvenient subjective tests in cases in which reference images are not available, it is desirable to develop an automatic no-reference image quality assessment (NR-IQA) technique. In this paper, a novel NR-IQA technique is proposed in which the distributions of local gradient orientations in image regions of different sizes are used to characterize an image. To evaluate the objective quality of an image, its luminance and chrominance channels are processed, as well as their high-order derivatives. Finally, statistics of used perceptual features are mapped to subjective scores by the support vector regression (SVR) technique. The extensive experimental evaluation on six popular IQA benchmark datasets reveals that the proposed technique is highly correlated with subjective scores and outperforms related state-of-the-art hand-crafted and deep learning approaches.

scholarly journals No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Multiple Scales ◽  
Digital Images ◽  
Input Image ◽  
Perceptual Quality ◽  
Reference Image ◽  
Benchmark Datasets ◽  
In The Wild

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

PENILAIAN KUALITI IMEJ DIGITAL BERDASARKAN KAEDAH CIRI-CIRI SISTEM PENGLIHATAN MANUSIA DAN PRINSIP STRUKTUR IMEJ

2016 ◽  
Vol 78 (5-10) ◽  
Author(s):  
Bahbibi Rahmatullah ◽  
Siti Tasnim Mahamud
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Visual Information ◽  
Image Quality Assessment ◽  
Digital Images ◽  
Reference Method ◽  
Assessment Method ◽  
Reference Image ◽  
Human Observer ◽  
Full Reference

Tremendous advances of information technology provide a large role for digital images for delivering information quickly and accurately. However, digital images are exposed to distortions and imperfect quality during acquisition, compression, transmission, processing and reproduction. Therefore, the development of effectively image quality assessment (IQA) is crucial in order to identify and measure the distortion in image quality. Perception by human observers (manually) as the ultimate receiver of the visual information contained in an image and most reliable to assess the quality of image. However, manual subjective assessment method is considered costly and time consuming. This lead to the development of proposed automatic method to measure image quality as accurately as the manual method. The goal of objective image quality assessment is to develop a computational model that can accurately and automatically predict the perceptual image quality. An ideal objective IQA method should be able to imitate the quality predictions of an average human observer. Full-reference image quality assessment is a method where image with perfect quality provided as a reference image for guiding the IQA system. This paper presents the study and comparison between two full-reference method that frequently used in IQA system that is method based on the properties of human visual system (HVS) and method based on principle of image structure. Both of this method is proven can be used to measure digital images quality accurately and depends on distortion types that occurred on measured images.

scholarly journals A Regression-Based Family of Measures for Full-Reference Image Quality Assessment

2016 ◽  
Vol 16 (6) ◽  
pp. 316-325 ◽  
Author(s):  
Mariusz Oszust
Keyword(s):  
Image Quality ◽  
Visual Perception ◽  
Quality Assessment ◽  
Quality Evaluation ◽  
Image Quality Assessment ◽  
Human Subjects ◽  
Reference Image ◽  
Human Visual Perception ◽  
Full Reference ◽  
Highly Correlated

Abstract The advances in the development of imaging devices resulted in the need of an automatic quality evaluation of displayed visual content in a way that is consistent with human visual perception. In this paper, an approach to full-reference image quality assessment (IQA) is proposed, in which several IQA measures, representing different approaches to modelling human visual perception, are efficiently combined in order to produce objective quality evaluation of examined images, which is highly correlated with evaluation provided by human subjects. In the paper, an optimisation problem of selection of several IQA measures for creating a regression-based IQA hybrid measure, or a multimeasure, is defined and solved using a genetic algorithm. Experimental evaluation on four largest IQA benchmarks reveals that the multimeasures obtained using the proposed approach outperform state-of-the-art full-reference IQA techniques, including other recently developed fusion approaches.

Image Quality Assessment Method Based on Fractional Fourier Transform

2013 ◽  
Vol 433-435 ◽  
pp. 372-375
Author(s):  
Bin Wang
Keyword(s):  
Fourier Transform ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Fractional Fourier Transform ◽  
Quality Score ◽  
Reference Image ◽  
Support Vector ◽  
Two Dimensional ◽  
Assessment Approach

Image quality assessment is an important issue in the area of image processing, and the no-reference image quality assessment tries to evaluate the quality of image without the reference image. The present no-reference image quality assessment approach can not predict the quality score accurately. This paper proposes a new image quality assessment approach based on two-dimensional discrete fractional Fourier transform (FRFT). After the image is processed by two dimensional discrete fourier transform, the histogram of FRFT coefficients in different order are modeled by generalized Gaussian distribution (GGD). The parameters of GGD are estimated and the feature vector is formed by parameters of GGD. After that, the image is classified into five distortion type by the trained support vector machine. At last, the quality score is predicted by the trained support vector regression machine. The experiment results show that the performance of proposed method is better than the traditional method.

scholarly journals Combined Full-Reference Image Quality Metrics for Objective Assessment of Multiply Distorted Images

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2256
Author(s):  
Krzysztof Okarma ◽  
Piotr Lech ◽  
Vladimir V. Lukin
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Objective Assessment ◽  
Assessment Methods ◽  
Subjective Quality ◽  
Reference Image ◽  
Exponential Weights ◽  
Full Reference ◽  
Highly Correlated

In the recent years, many objective image quality assessment methods have been proposed by different researchers, leading to a significant increase in their correlation with subjective quality evaluations. Although many recently proposed image quality assessment methods, particularly full-reference metrics, are in some cases highly correlated with the perception of individual distortions, there is still a need for their verification and adjustment for the case when images are affected by multiple distortions. Since one of the possible approaches is the application of combined metrics, their analysis and optimization are discussed in this paper. Two approaches to metrics’ combination have been analyzed that are based on the weighted product and the proposed weighted sum with additional exponential weights. The validation of the proposed approach, carried out using four currently available image datasets, containing multiply distorted images together with the gathered subjective quality scores, indicates a meaningful increase of correlations of the optimized combined metrics with subjective opinions for all datasets.

No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

2020 ◽  
Vol 64 (1) ◽  
pp. 10505-1-10505-16
Author(s):  
Yin Zhang ◽  
Xuehan Bai ◽  
Junhua Yan ◽  
Yongqi Xiao ◽  
C. R. Chatwin ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Method ◽  
Image Distortion ◽  
Image Feature ◽  
Reference Image ◽  
Gradient Magnitude ◽  
Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

Sign in / Sign up

Export Citation Format

Share Document