scholarly journals Quality Assessment of Digital Picture. Subjective Assessment Techniques of Digital Image Quality.

1999 ◽  
Vol 53 (9) ◽  
pp. 1195-1198 ◽  
Author(s):  
Tetsuya Mitsuhashi
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Digital Image ◽  
Subjective Assessment ◽  
Digital Picture ◽  
Assessment Techniques

scholarly journals Digital image contrast assessment based on the Weibull distribution parameters

2021 ◽  
pp. 4-13
Author(s):  
Y. I. Golub ◽  
F. V. Starovoitov
Keyword(s):  
Image Quality ◽  
Weibull Distribution ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Numerical Estimate ◽  
Rank Correlation ◽  
Subjective Assessment ◽  
Reference Image ◽  
Local Contrast ◽  
Two 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.

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

2018 ◽  
pp. 1322-1337
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 ◽  
Natural Scenes ◽  
Assessment Scores ◽  
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.

scholarly journals NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION

2018 ◽  
Vol 37 (2) ◽  
pp. 105 ◽  
Author(s):  
Kanjar De ◽  
Masilamani V
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Multiple Sources ◽  
Noise Image ◽  
Assessment Techniques ◽  
Image Quality Measure ◽  
Channel Errors

Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available. 

scholarly journals Blind Image Quality Assessment Using a CNN and Edge Distortion

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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