Digital image contrast assessment based on the Weibull distribution parameters

The goal of the studies described in the paper is to find a quantitative assessment that maximally correlates with the subjective assessment of the contrast image quality in the absence of reference image. As a result of the literature analysis, 16 functions were selected that are used for no-refernce image quality assessment: BEGH, BISH, BREN, CMO, CURV, FUS, HELM, EBCM, KURT, LAPD, LAPL, LAPM, LOCC, LOEN, SHAR, WAVS. They all use the arithmetical mean of the local contrast quality. As an alternative to averaging local estimates (since the mean is one of two parameters of the normal distribution), it is proposed to use one of two parameters of the Weibull distribution of the same data – scale or shape.For the experiments, digital images with nonlinear contrast distortion from the available CCID2014 database were used. It contains 15 original images with a size of 768x512 pixels and 655 versions with modified contrast. This database of images contains the average visual quality assessment (Mean Opinion Score, briefly MOS) of each image. Spearman’s rank correlation coefficient was used to determine the correspondence between the visual MOS scores and the studied quantitative measures.As a result of the research, a new quality assessment measure of contrast images in the absence of references is presented. To obtain the estimate, local quality values are calculated by the BREN measure, their set is described by the Weibull distribution, and the scale parameter of the distribution serves as the best numerical estimate of the quality of contrast images. This conclusion is confirmed experimentally, and the proposed measure correlates better than other variants with the subjective assessments of experts.

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Blind Image Quality Assessment Using a CNN and Edge Distortion

Revue d intelligence artificielle ◽

10.18280/ria.350406 ◽

2021 ◽

Vol 35 (4) ◽

pp. 315-324

Author(s):

Rajesh Babu Movva ◽

Raja Kumar Kontham

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Subjective Assessment ◽

Image Features ◽

Reference Image ◽

Linear Transformations ◽

Blind Image Quality Assessment ◽

Kernel Approach ◽

Blind Image

The present paper introduces a Convolutional Neural Network (CNN) for the assessment of image quality without a reference image, which comes under the category of Blind Image Quality Assessment models. Edge distortions in the image are characterized as input feature vectors. This approach is in justification of the fact that subjective assessment focusses on image features that emanate from the edges and the boundaries present in the image. The earlier methods were found to use complex transformations on the image to extract the features before training or as a part of the training. The present work uses Prewitt kernel approach to extract the horizontal and vertical edge maps of the training images. These maps are then input to a simple CNN for extracting higher level features using non-linear transformations. The resultant features are mapped to image quality score by regression. The network uses Spatial Pyramid Pooling (SPP) layer to accommodate input images of varying sizes. The present proposed model was tested on popular datasets used in the domain of Image Quality Assessment (IQA). The experimental results have shown that the model competes with the earlier proposed models with simplicity of feature extraction and involvement of minimal complexity.

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No-Reference Image Quality Assessment Based on Visual Perception

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.1.325 ◽

2011 ◽

Vol 1 ◽

pp. 325-329

Author(s):

Jun Ni ◽

Zi Yin Li ◽

Hua Cai Chen

Keyword(s):

Image Quality ◽

Visual Perception ◽

Quality Assessment ◽

Video Compression ◽

Image Quality Assessment ◽

Subjective Assessment ◽

Assessment Model ◽

Reference Image ◽

Probability Summation ◽

Different Content

No-reference image quality assessment is an important issue for video compression and communication. This work presents a no-reference objective image/video sharpness method based on visual perception metric (VPM). The algorithm gets image typical edge and edge width firstly, and then gets gray contrast of typical edge region, finally utilizes these factors to integrate a probability summation assessment model. The proposed metric is able to predict the amount of sharpness in image with different content. Experimental results show that this method is consistent with subjective assessment of human being and can be use to describe the visual perception of image effectively.

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Blind Assessment of Wavelet-Compressed Images Based On Subband Statistics of Natural Scenes

International Journal of Advanced Pervasive and Ubiquitous Computing ◽

10.4018/ijapuc.2014010103 ◽

2014 ◽

Vol 6 (1) ◽

pp. 26-43

Author(s):

Yingchun Guo ◽

Gang Yan ◽

Cuihong Xue ◽

Yang Yu

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Correlation Coefficient ◽

Image Quality Assessment ◽

Pearson Correlation ◽

Correlation Coefficients ◽

Subjective Assessment ◽

Reference Image ◽

Natural Scenes ◽

Compressed Images

This paper presents a no-reference image quality assessment metric that makes use of the wavelet subband statistics to evaluate the levels of distortions of wavelet-compressed images. The work is based on the fact that for distorted images the correlation coefficients of the adjacent scale subbands change proportionally with respect to the distortion of a compressed image. Subband similarity is used in this work to measure the correlations of the adjacent scale subbands of the same wavelet orientations. The higher the image quality is (i.e., less distortion), the greater the cosine similarity coefficient will be. Statistical analysis is applied to analyze the performance of the metric by evaluating the relationship between the human subjective assessment scores and the subband cosine similarities. Experimental results show that the proposed blind method for the quality assessment of wavelet-compressed images has sufficient prediction accuracy (high Pearson Correlation Coefficient, PCCs), sufficient prediction monotonicity (high Spearman Correlation Coefficient SCCs) and sufficient prediction consistency (low outlier ratios) and less running time. It is simple to calculate, has a clear physical meaning, and has a stable performance for the four image databases on which the method was tested.

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

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.1.010505 ◽

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.

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