scholarly journals ALGORITHM AND METHODS OF RANKING GROUP OF BITMAP IMAGES

2021 ◽  
Vol 2021 (4) ◽  
pp. 58-67
Author(s):  
Viktor Afonin ◽  
Anastasia Vasilevna Savkina ◽  
Vladimir Nikulin
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Rank Correlation ◽  
Average Error ◽  
Color Model ◽  
Absolute Maximum ◽  
Statistical Parameters ◽  
Color Models ◽  
Rgb Image

The article presents an algorithm and a methodology of ranking a group of raster images by using the criterion of their expected quality. Ranking refers to the evaluation of a sample of bitmap images in a descending order of their quality, the image quality assessment being performed on the basis of a number of statistical parameters, such as coefficients of variation, determination, rank correlation index, as well as errors (absolute maximum error, average error, average quadratic error). The differences between the images are based on converting a full-color RGB image into HSV, Lab, NTSC, XYZ, YCbCr color models, which are represented as one-dimensional pixel ma-trices. The colour model RGB is taken as a reference. In relation to it, the proposed statistical char-acteristics of other color models are compared, any object of each color model being compared with the base model - an RGB image. Based on this comparison, all images of a given group are analyzed independently of each other. Image quality assessment is performed in a module that can be used to cycle through multiple images and is represented in numerical form as a real number. One of the module blocks calculates the statistical parameters between each color model and the base RGB model. After receiving the values of the quality scores they are ranked according to their values. As a result, an image with a higher or lower scene quality can be determined. Images with blocking artifacts, noisy images of the salt & pepper type, and images with strobe effects artifacts were considered as test images.

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.

A Novel Hybrid Image Quality Assessment Algorithm

2011 ◽  
Vol 4 (4) ◽  
pp. 107-108
Author(s):  
Deepa Maria Thomas ◽  
◽  
S. John Livingston
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

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.

Image quality assessment based on local invariant features

2013 ◽  
Vol 32 (12) ◽  
pp. 3369-3372 ◽  
Author(s):  
Ya-zhou YANG ◽  
Xiao-qing YING ◽  
Guang-quan CHENG ◽  
Dan TU
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Local Invariant ◽  
Invariant Features ◽  
Local Invariant Features

Overview on Image Quality Assessment Methods

2010 ◽  
Vol 2010 (1) ◽  
pp. 219-226 ◽  
Author(s):  
Gang-yi Jiang ◽  
Da-jiang Huang ◽  
Xu Wang ◽  
Mei Yu
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Assessment Methods
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