Evaluating image quality using consistent grey relational grade

2014 ◽  
Vol 31 (2) ◽  
pp. 231-249 ◽  
Author(s):  
Yen-Ching Chang ◽  
Chun-Ming Chang ◽  
Liang-Hwa Chen ◽  
Tung-Jung Chan
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Quality Evaluation ◽  
New Approach ◽  
Content Type ◽  
Multi Criteria Evaluation ◽  
Grey Relational Grade ◽  
Enhancement Method ◽  
Efficient Scheme ◽  
Grey Relational

Purpose – Assessing image quality is a difficult task. Different demands need distinct criteria, so it is not realistic to decide which contrast enhancement method is better only through one criterion. The main purpose is to propose an efficient scheme to effectively evaluate image quality. Furthermore, the idea can be applied in other fields. Design/methodology/approach – To objectively and quantitatively assess image quality, the authors integrate four criteria into one composite criterion and use it to evaluate seven existing contrast enhancement methods. The mechanism of integration is through a newly proposed way of computing a grey relational grade (GRGd), called the consistent grey relational grade (CGRGd). Findings – In this paper, the authors propose the CGRGd, which is more efficient and consistent than other existing GRGds. When applied to image quality evaluation, the proposed CGRGd can effectively choose the best method than others. The results also indicate that the proposed CGRGd combined with appropriate criteria can be widely used in the field of multiple criteria. Originality/value – The proposed CGRGd is a new approach to the problem of multi-criteria evaluation, and its application to the evaluation of image quality is a novel idea. For readers interested in the field of multi-criteria decision-making, the CGRGd provides an efficient and effective alternative.

A Multi-Criteria Image Quality Evaluation Scheme

2013 ◽  
Vol 284-287 ◽  
pp. 2975-2979
Author(s):  
Yen Ching Chang ◽  
Chun Ming Chang ◽  
Liang Hwa Chen ◽  
Tung Jung Chan
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Quality Evaluation ◽  
Experimental Results ◽  
Human Vision ◽  
Effective Mechanism ◽  
Image Quality Evaluation ◽  
Evaluation Scheme ◽  
Grey Relational Grade ◽  
Grey Relational

It is difficult to objectively and quantitatively judge image quality by a single criterion, such as contrast. In general, excessive contrast enhancement easily leads to a loss of image quality. Thus, it easily gives a wrong evaluation to rank image quality according to contrast values. In order to achieve a consistent result with human vision perception, balancing multi-criteria will be a feasible approach. Therefore, we propose a multi-criteria image quality evaluation scheme for ranking seven existing contrast enhancement methods. The scheme applies four criteria to a newly proposed way of computing a grey relational grade (GRGd), called the consistent grey relational grade (CGRGd). Experimental results show that our proposed CGRGd do provides a very effective mechanism to choose the best method for a specific purpose.

Study on the second grey relational grade and its properties

Kybernetes ◽  
2010 ◽  
Vol 39 (8) ◽  
pp. 1330-1335 ◽  
Author(s):  
Yan Ma
Keyword(s):  
System Theory ◽  
Design Methodology ◽  
Grey System Theory ◽  
Forecasting Model ◽  
Grey System ◽  
Content Type ◽  
Grey Forecasting ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Practical Implications

PurposeThe purpose of this paper is to propose a second relational grade based on the general grey relational grade and analyze several of its properties.Design/methodology/approachGrey system theory. The paper proposes and studies second grey relational grade, establishes second grey relational formula, and studies several characteristics of second grey relational formula.FindingsProposing a second relational grade proved it could solve the problem of the parallelism partly and weaken relativity of space position.Research limitations/implicationsUntil now, the problem of the consistency could not be solved, nor could the problem of the effect which keeps the sequence the same.Practical implicationsThe precision of the grey forecasting model could be strengthened if used in the forecasting model.Originality/valueThe general relational grade only thinks over the relation between two sequences but does not involve the relation in one sequence. The second relational grade considers these two, so if the forecasting model is established with it, the model should be more exact.

scholarly journals Methods to Increase the Contrast of the Image with Preserving the Visual Quality

2021 ◽  
Vol 6 (2) ◽  
pp. 140-145
Author(s):  
Mykola Maksymiv ◽  
◽  
Taras Rak
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Contrast Enhancement ◽  
Image Quality Assessment ◽  
Visual Quality ◽  
Image Contrast ◽  
Weighted Sum ◽  
Enhancement Method ◽  
Optimal Target ◽  
Statistical Metrics

Contrast enhancement is a technique for increasing the contrast of an image to obtain better image quality. As many existing contrast enhancement algorithms typically add too much contrast to an image, maintaining visual quality should be considered as a part of enhancing image contrast. This paper focuses on a contrast enhancement method that is based on histogram transformations to improve contrast and uses image quality assessment to automatically select the optimal target histogram. Improvements in contrast and preservation of visual quality are taken into account in the target histogram, so this method avoids the problem of excessive increase in contrast. In the proposed method, the optimal target histogram is the weighted sum of the original histogram, homogeneous histogram and Gaussian histogram. Structural and statistical metrics of “naturalness of the image” are used to determine the weights of the corresponding histograms. Contrast images are obtained by matching the optimal target histogram. Experiments show that the proposed method gives better results compared to other existing algorithms for increasing contrast based on the transformation of histograms.

The proof of a new modified grey relational grade

2016 ◽  
Vol 6 (2) ◽  
pp. 180-186 ◽  
Author(s):  
Kunli Wen
Keyword(s):  
System Theory ◽  
Grey Relational Analysis ◽  
Design Methodology ◽  
Grey System Theory ◽  
Grey System ◽  
Content Type ◽  
The Past ◽  
Relational Analysis ◽  
Grey Relational Grade ◽  
Grey Relational

Purpose – Until now, many different varieties of grey relational grade methods had been proposed, and there are also many relevant publications. However, in one article published in 2007, which applied the previous grey relational grade to environmental protection fields and some results had been found. After studied it carefully, the author found that the grey relational grade in the paper was not the previous grey relational grade. According to the mathematics logic, it must first prove the proposed grey relational grade satisfies the four axioms in grey relational analysis, and then the author can say that the achieved results are reasonable and correct. The paper aims to discuss these issues. Design/methodology/approach – The paper lists the rational and regular grey relational grade that had been published in the past, and used the four axioms in grey system theory to prove the Pai’s grey relational grade that satisfy the four axioms steps by steps. Findings – Through the detail proof of the proposed grey relational grade in Pai’s paper, it indeed satisfies the four axioms in grey relational grade. Research limitations/implications – The paper had enhanced the correctness and reasonableness of that paper, and let the grey relational grade, which appear in Pai’s paper is legitimate and correct grey relational grade in grey system theory. Originality/value – The paper had identified that Pai’s grey relational grade is a rational and regular grey relational grade in grey system theory, and it proves that the results in Pai’s paper are correct and reasonable.

Influence of Tool Nose Radius and Cutting Performance during Face Milling of Magnesium Alloy for Output Responses by Taguchi based Grey Relation Analysis

2021 ◽  
Vol 13 (2) ◽  
Author(s):  
Durai Kumaran ◽  
S.P. Sundar Singh Sivam ◽  
Harshavardhana Natarajan ◽  
P.R. Shobana Swarna Ratna
Keyword(s):  
Removal Rate ◽  
Cutting Speed ◽  
Face Milling ◽  
Machining Process ◽  
Tool Geometry ◽  
Depth Of Cut ◽  
Machining Parameters ◽  
New Approach ◽  
Grey Relational Grade ◽  
Grey Relational

In order to take advantage of the machining characteristics of magnesium, it is useful to consider recommended tool design and angles. The geometry of the tool can have a large influence on the machining process. Tool geometry can be used to aid with chip flow and clearance, reduce excessive heat generation, reduce tool build up, enable greater feed rates to be employed and improved tool life. This paper presents a new approach for the optimization of machining parameters on face milling of ZE41 with multiple responses based on orthogonal array with grey relational analysis. Machining tests are carried out by inserting 12 mm diameter of insert having 1 flute under dry condition. In this study, machining parameters namely cutting speed, feed and depth of cut and tool node radius are optimized with the considerations of multi responses such as surface roughness, material removal rate, tool wear and thrust force. A grey relational grade is obtained from the grey analysis. Based on the grey relational grade, optimum levels of parameters have been identified and significant contribution of parameters is determined by ANOVA. Confirmation test is conducted to validate the test result. Experimental results have shown that the responses in Machining process can be improved effectively through the new approach.

An uncertain pure linguistic approach on evaluation of enterprise integrity based on grey information

2016 ◽  
Vol 6 (3) ◽  
pp. 353-364 ◽  
Author(s):  
Zhen Zhen Ma ◽  
Jianjun Zhu
Keyword(s):  
Ideal Point ◽  
Grey System Theory ◽  
Decision Makers ◽  
Fuzzy Evaluation ◽  
Grey System ◽  
Content Type ◽  
Grey Relational Grade ◽  
Grey Number ◽  
Linguistic Approach ◽  
Grey Relational

Purpose Currently, for the evaluation of enterprise credit, many specific values of indexes are difficult to obtain, so decision makers tend to give a form of uncertain linguistic variable. To solve this kind of problem, the purpose of this paper is to introduce an uncertain pure linguistic approach on evaluation of enterprise integrity based on grey information. Design/methodology/approach Initial uncertain linguistic variables given by experts are transferred into interval grey numbers, and their greyness of degree is computed. Then, the greyness of degree is applied to adjust the weights of experts. Moreover, the core of each interval grey number is calculated, and through giving the positive ideal point and negative ideal point, which are binary numbers, the comprehensive grey relational grade between the linguistic number and the two points is calculated, respectively, as well to get the ranking result of projects by considering both core and greyness of degree. Findings The model is applied to a case, and the result verifies the validity and practicability of the model which reveals high effectiveness. Practical implications This model provides a new feasible method in a growing number of fuzzy evaluation schemes in the fields of enterprise integrity and contributes to getting better and more accurate results. Originality/value In this paper, the greyness of degree is introduced to the model to adjust the experts’ weights, and it reflects the thought of “making full use of the information” in grey system theory and further enriches the system of grey decision-making theory as well as expanding its application scope.

The quantized transformation in Deng’s grey relational grade

2016 ◽  
Vol 6 (3) ◽  
pp. 375-397 ◽  
Author(s):  
Kunli Wen
Keyword(s):  
Design Methodology ◽  
Entropy Method ◽  
Grey System Theory ◽  
Mathematics Model ◽  
Careful Study ◽  
Grey System ◽  
Content Type ◽  
Grey Relational Grade ◽  
Grey Relational ◽  
Practical Implications

Purpose Until now, many different varieties of grey relational grade methods had been proposed, and there are also many relevant publications, which include ordinal grey relational grade and cardinal grey relational grade. However, the most original and important formula is Deng’s grey relational grade. After careful study it was found that although it is an ordinal form of grey relational grade, a rational mathematics model can be used to transfer it from ordinal into cardinal. It not only can enhance the essential of Deng’s grey relational grade, but also can let Deng’s grey relational grade be used more widely. The paper aims to discuss these issue. Design/methodology/approach The paper uses fuzzy set theory to get the rational value of distinguish coefficient in Deng’s grey relational grade, then uses grey entropy method to decide the rational weighting for the analysis sequences in Deng’s grey relational grade. Findings Through the mathematics derivation, it indeed can transfer the Deng’s grey relational grade from ordinal form into cardinal form. Practical implications The paper has deeply enhanced the essential of Deng’s grey relational grade, and made Deng’s grey relational grade more available and more usable in grey system theory. Originality/value The paper has transferred the Deng’s grey relational grade from ordinal into cardinal, it can let Deng’s grey relational grade be used in a wider area.

Image quality evaluation of a small animal PET scanner

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Andréa Vidal Ferreira ◽  
Rodrigo Modesto Gadelha Gontijo ◽  
Guilherme Cavalcante de Albuquerque Souza ◽  
Bruno Melo Mendes ◽  
Juliana Batista da Silva ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Small Animal ◽  
Small Animal Pet ◽  
Image Quality Evaluation ◽  
Animal Pet

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

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