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

Saliency Map ◽

Reference Image ◽

Support Vector ◽

Space Domain ◽

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.

Blind Image Quality Assessment Based on Natural Statistics of Double-Opponency

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2018.p0725 ◽

2018 ◽

Vol 22 (5) ◽

pp. 725-730 ◽

Cited By ~ 1

Author(s):

Edwin Sybingco ◽

◽

Elmer P. Dadios

Keyword(s):

Neural Network ◽

Image Quality ◽

Quality Assessment ◽

Gaussian Distribution ◽

Assessment Method ◽

Feedforward Neural Network ◽

Reference Image ◽

Natural Statistics

One of the challenges in image quality assessment (IQA) is to determine the quality score without the presence of the reference image. In this paper, the authors proposed a no-reference image quality assessment method based on the natural statistics of double-opponent (DO) cells. It utilizes the statistical modeling of the three opponency channels using the generalized Gaussian distribution (GGD) and asymmetric generalized Gaussian distribution (AGGD). The parameters of GGD and AGGD are then applied to feedforward neural network to predict the image quality. Result shows that for any opposing channels, its natural statistics parameters when applied to feedforward neural network can achieve satisfactory prediction of image quality.

No-Reference Image Quality Assessment Based on Dual-Domain Feature Fusion

Entropy ◽

10.3390/e22030344 ◽

2020 ◽

Vol 22 (3) ◽

pp. 344

Author(s):

Yueli Cui

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Gaussian Distribution ◽

Feature Fusion ◽

Assessment Model ◽

Reference Image ◽

Dual Domain ◽

Fitting Parameters

Image quality assessment (IQA) aims to devise computational models to evaluate image quality in a perceptually consistent manner. In this paper, a novel no-reference image quality assessment model based on dual-domain feature fusion is proposed, dubbed as DFF-IQA. Firstly, in the spatial domain, several features about weighted local binary pattern, naturalness and spatial entropy are extracted, where the naturalness features are represented by fitting parameters of the generalized Gaussian distribution. Secondly, in the frequency domain, the features of spectral entropy, oriented energy distribution, and fitting parameters of asymmetrical generalized Gaussian distribution are extracted. Thirdly, the features extracted in the dual-domain are fused to form the quality-aware feature vector. Finally, quality regression process by random forest is conducted to build the relationship between image features and quality score, yielding a measure of image quality. The resulting algorithm is tested on the LIVE database and compared with competing IQA models. Experimental results on the LIVE database indicate that the proposed DFF-IQA method is more consistent with the human visual system than other competing IQA methods.

Image Quality Assessment Method Based on Fractional Fourier Transform

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.433-435.372 ◽

2013 ◽

Vol 433-435 ◽

pp. 372-375

Author(s):

Bin Wang

Keyword(s):

Fourier Transform ◽

Image Quality ◽

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.

Full Reference Image Quality Assessment Based on Saliency Map Analysis

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2010.54.3.030503 ◽

2010 ◽

Vol 54 (3) ◽

pp. 030503 ◽

Cited By ~ 33

Author(s):

Yubing Tong ◽

Hubert Konik ◽

Faouzi A. Cheikh ◽

Alain Tremeau

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Saliency Map ◽

Reference Image ◽

Full Reference ◽

Map Analysis

Image similarity metric (ISIM): a reduced reference image quality assessment approach

CSI Transactions on ICT ◽

10.1007/s40012-015-0066-8 ◽

2015 ◽

Vol 3 (1) ◽

pp. 1-11 ◽

Cited By ~ 7

Author(s):

Vikrant Bhateja ◽

Aseem Kalsi ◽

Aastha Srivastava

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Similarity ◽

Reference Image ◽

Similarity Metric ◽

Assessment Approach

2013 International Conference on Signal-Image Technology & Internet-Based Systems ◽

Color Image Quality Assessment Measure Using Multivariate Generalized Gaussian Distribution

10.1109/sitis.2013.42 ◽

2013 ◽

Cited By ~ 5

Author(s):

Mounir Omari ◽

Abdelkaher Ait Abdelouahad ◽

Mohammed El Hassouni ◽

Hocine Cherifi

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Gaussian Distribution ◽

Color Image ◽

Multivariate Generalized Gaussian Distribution

Assessment Measure ◽

AN IMAGE QUALITY METRIC BASED ON BIOLOGICALLY INSPIRED FEATURE MODEL

International Journal of Image and Graphics ◽

10.1142/s0219467811004093 ◽

2011 ◽

Vol 11 (02) ◽

pp. 265-279 ◽

Cited By ~ 2

Author(s):

CHENG DENG ◽

JIE LI ◽

YIFAN ZHANG ◽

DONGYU HUANG ◽

LINGLING AN

Keyword(s):

Image Quality ◽

Similarity Index ◽

Saliency Map ◽

Feature Weighting ◽

Feature Model ◽

Perceptual Quality ◽

Reference Image ◽

Biologically Inspired ◽

Distorted Image

Objective image quality assessment (IQA) metrics have been widely applied to imaging systems to preserve and enhance the perceptual quality of images being processed and transmitted. In this paper, we present a novel IQA metric based on biologically inspired feature model (BIFM) and structural similarity index (SSIM). The SSIM index map is first generated through the well-known IQA metric SSIM between the reference image and the distorted image. Then, saliency map of the distorted image is extracted via BIF to define the most salient image locations. Finally, according to the saliency map, a feature weighting model is employed to define the different weights for the different samples in the SSIM index map. Experimental results confirm that the proposed IQA metric improves the performance over PSNR and SSIM under various distortion types in terms of different evaluation criteria.