Influence of image-quality degradation on accommodation mechanism in human vision: Conditions necessary for objective image-quality evaluation metrics

2012 ◽  
Vol 20 (7) ◽  
pp. 367-379 ◽  
Author(s):  
Toshikazu Matsui
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Human Vision ◽  
Evaluation Metrics ◽  
Image Quality Evaluation ◽  
Quality Degradation ◽  
Accommodation Mechanism

scholarly journals IMAGE QUALITY EVALUATION METRICS BASED ON HWD

2009 ◽  
Vol 28 (1) ◽  
pp. 72-76
Author(s):  
Wen LU ◽  
Xin-Bo GAO ◽  
Kai ZENG ◽  
Li-Huo HE
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Evaluation Metrics ◽  
Image Quality Evaluation

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.

Remote sensing image quality evaluation based on deep learning

2021 ◽  
pp. 1-9
Author(s):  
Tong Wang ◽  
Hemeng Yang ◽  
Ling Zhu ◽  
Yazhou Fan ◽  
Xue Yang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Quality Evaluation ◽  
Remote Sensing Image ◽  
Human Vision ◽  
Remote Sensing Images ◽  
Image Quality Evaluation ◽  
Remote Sensing Technology ◽  
Sensing Technology

Remote sensing technology is an effective tool for sensing the earth’s surface. With the continuous improvement of remote sensing technology, remote sensing detectors can obtain more spectral and spatial information, including clear feature contours, complex texture features and spatial layout rules. This information was detected in mineral resources. Surface substance identification, water pollution information monitoring and many other aspects have played an important role. The coding algorithm and defects, storage algorithm and interference from atmospheric cloud radiation information during the imaging process lead to varying degrees of distortion and deterioration of remote sensing images during imaging, transmission and storage. This makes it difficult to process, analyze and apply remote sensing images. Therefore, the design of a reasonable remote sensing image quality evaluation method is not only conducive to the remote sensing image quality evaluation in the real-time processing system of remote sensing image, but also conducive to the optimization of remote sensing image system and image processing algorithm. The application is worthwhile. In this paper, the deteriorating features of remote sensing images will change the statistical distribution. We propose a method for evaluating the quality of remote sensing images in depth learning. Feature learning and blurring as well as noise intensity classification for image remote sensing using convolutional neural network are carried out. The evaluation model is modified by masking effect and perceptual weighting factor, and the quality evaluation results of remote sensing images are obtained according to human vision. The research shows that this method can effectively solve the problem of removing and evaluating the noise of remote sensing image, and can effectively and accurately evaluate the quality of remote sensing image. It is also consistent with subjective assessment and human perception.

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

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