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 Quality Assessment on Authentically Distorted Images by Expanding Proxy Labels

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 252 ◽  
Author(s):  
Xiaodi Guan ◽  
Fan Li ◽  
Lijun He
Keyword(s):  
Image Quality ◽  
Visual Perception ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Image Features ◽  
Quality Score ◽  
Fine Tuning ◽  
Reference Image ◽  
Human Visual Perception ◽  
In The Wild

In this paper, we propose a no-reference image quality assessment (NR-IQA) approach towards authentically distorted images, based on expanding proxy labels. In order to distinguish from the human labels, we define the quality score, which is generated by using a traditional NR-IQA algorithm, as “proxy labels”. “Proxy” means that the objective results are obtained by computer after the extraction and assessment of the image features, instead of human judging. To solve the problem of limited image quality assessment (IQA) dataset size, we adopt a cascading transfer-learning method. First, we obtain large numbers of proxy labels which denote the quality score of authentically distorted images by using a traditional no-reference IQA method. Then the deep network is trained by the proxy labels, in order to learn IQA-related knowledge from the amounts of images with their scores. Ultimately, we use fine-tuning to inherit knowledge represented in the trained network. During the procedure, the mapping relationship fits in with human visual perception closer. The experimental results demonstrate that the proposed algorithm shows an outstanding performance as compared with the existing algorithms. On the LIVE In the Wild Image Quality Challenge database and KonIQ-10k database (two standard databases for authentically distorted image quality assessment), the algorithm realized good consistency between human visual perception and the predicted quality score of authentically distorted images.

Gauss-guided patch-based deep convolutional neural networks for no-reference image quality assessment

2021 ◽  
pp. 1-10
Author(s):  
Ze-Nan Zhou ◽  
Zhiheng Zhou ◽  
Junchu Huang
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Perceived Quality ◽  
Mean Value ◽  
Quality Score ◽  
Reference Image ◽  
Deep Convolutional Neural Networks ◽  
Global Quality ◽  
Local Quality

Patch-based deep convolutional neural network (DCNN) has been proved to have advanced performance in no-reference image quality assessment (NR-IQA). However, these methods generally take global quality score as the quality score of each patch mainly since local quality score is not provided. Unfortunately, the perceived quality of image patch is difficult to maintain a high degree of consistency. Thus, the use of the same global quality score in different patches of the same image may hinder training of DCNNs. In this paper, we propose a universal and nearly cost-free model called Gaussian Random Jitter (GRJ). According to the uncertainty of the perceived quality, GRJ divided the training images into high-confidence distorted images and low-confidence distorted images, and reasonably assigned different local quality scores to each patch through specific gaussian functions with the global quality score as the mean value and the undetermined hyperparameter as the standard deviation. We took one of the most advanced patch-based DCNNs models as backbone and tested the improved performance over three widely used image quality databases. We show that our model can further improve the performance of patch-based models and even help them comparable with those of state-of-the-art NR-IQA algorithms.

An Image Quality Assessment Approach Based on Saliency Map in Space Domain

2014 ◽  
Vol 1006-1007 ◽  
pp. 768-772
Author(s):  
Bin Wang
Keyword(s):  
Image Quality ◽  
Gaussian Distribution ◽  
Image Quality Assessment ◽  
Saliency Map ◽  
Reference Image ◽  
Support Vector ◽  
Space Domain ◽  
Generalized Gaussian Distribution ◽  
Assessment Approach ◽  
Recent Method

This paper proposes a no-reference image assessment approach (IQA) based on saliency map in the space domain of the image. The saliency map of the image is extracted by Itti model at first. Next, the saliency-map weighted normalized image is used to get the histogram of the image, then the histogram is modeled by generalized gaussian distribution and the parameter of the generalized gaussian distribution is estimated by parameter estimating approach. Parameters of the generalized gaussian distribution are used as the feature vector for the training and testing image. The feature vectors of the testing image are fed to support vector regresion machine to evaluate the image quality score. Experimental results show that our approach outperforms the recent method of no-reference IQA.

Non reference image quality assessment approach based on standard model features of biological vision

2014 ◽  
Vol 29 (6) ◽  
pp. 1016-1023
Author(s):  
杨亚威 YANG Yawei ◽  
李俊山 LI Junshan ◽  
张士杰 ZHANG Shijie ◽  
芦鸿雁 LU Hongyan ◽  
胡双演 HU Shuangyan
Keyword(s):  
Image Quality ◽  
Standard Model ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Biological Vision ◽  
Assessment Approach ◽  
Model Features

Tetrolet-based reduced reference image quality assessment approach

2014 ◽  
Author(s):  
Abdelkaher Ait Abdelouahad ◽  
Mounir Omari ◽  
Hocine Cherifi ◽  
Brahim Alibouch ◽  
Mohammed El Hassouni
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Assessment Approach

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.

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