Special issue Visual system and image technology. 7. Image quality of hard copies. 4. Image quality evaluation in silver halide photography.

This paper presents an image quality evaluation of privacy filters for CRT display workstations. A photometric procedure was developed to evaluate the optical quality of privacy filters across horizontal display viewing angles. Then, the procedure was applied to two commercially-available privacy filter products. The results of the optical evaluation were compared with subjective image quality judgments of the privacy-filtered CRTs viewed under various ambient illumination, screen contrast polarity, and viewing angle conditions. The findings establish a human factors basis and procedure for objectively characterizing the image quality of privacy filters used on CRT displays.

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Remote sensing image quality evaluation based on deep learning

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-219109 ◽

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.

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Special issue Visual system and image technology. 7. Image quality of hard copies. 2. Digital halftoning.

The Journal of the Institute of Television Engineers of Japan ◽

10.3169/itej1978.40.317 ◽

1986 ◽

Vol 40 (4) ◽

pp. 317-323

Author(s):

Hiroaki Kotera

Keyword(s):

Image Quality ◽

Visual System ◽

Digital Halftoning ◽

Special Issue

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Image quality evaluation: JPEG 2000 versus intra-only H.264/AVC High Profile

Facta universitatis - series Electronics and Energetics ◽

10.2298/fuee0701071c ◽

2007 ◽

Vol 20 (1) ◽

pp. 71-83 ◽

Cited By ~ 6

Author(s):

Sang-Gyu Cho ◽

Zoran Bojkovic ◽

Dragorad Milovanovic ◽

Jungsik Lee ◽

Jae-Jeong Hwang

Keyword(s):

Image Quality ◽

Quality Evaluation ◽

Color Image ◽

Jpeg 2000 ◽

Image Quality Evaluation ◽

High Profile ◽

Block Transform ◽

Blocking Artifact ◽

Image Transform

The objective of this work is to provide image quality evaluation for intra-only H.264/AVC High Profile (HP) standard versus JPEG2000 standard. Here, we review the structure of the two standards and the coding algorithms in the context of subjective and objective assessments. Simulations were performed on a test set of monochrome and color image. As a result of simulations, we observed that the subjective and objective image quality of H.264/AVC is superior to JPEG2000, except the blocking artifact which is inherent, since it consists of block transform rather than whole image transform. Thus, we propose a unified measurement system to properly define image quality.

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Special issue Visual system and image technology. 7. Image quality of hard copies. 1. Introduction.

The Journal of the Institute of Television Engineers of Japan ◽

10.3169/itej1978.40.312 ◽

1986 ◽

Vol 40 (4) ◽

pp. 312-317

Author(s):

Kazuo Sayanagi

Keyword(s):

Image Quality ◽

Visual System ◽

Special Issue

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Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy

Entropy ◽

10.3390/e21111070 ◽

2019 ◽

Vol 21 (11) ◽

pp. 1070 ◽

Cited By ~ 1

Author(s):

Jinhua Liu ◽

Mulian Xu ◽

Xinye Xu ◽

Yuanyuan Huang

Keyword(s):

Neural Network ◽

Wavelet Transform ◽

Image Quality ◽

Information Entropy ◽

Quality Evaluation ◽

Evaluation Method ◽

Visual Quality ◽

Image Quality Evaluation ◽

Band Image

The image quality evaluation method, based on the convolutional neural network (CNN), achieved good evaluation performance. However, this method can easily lead the visual quality of image sub-blocks to change with the spatial position after the image is processed by various distortions. Consequently, the visual quality of the entire image is difficult to reflect objectively. On this basis, this study combines wavelet transform and CNN method to propose an image quality evaluation method based on wavelet CNN. The low-frequency, horizontal, vertical, and diagonal sub-band images decomposed by wavelet transform are selected as the inputs of convolution neural network. The feature information in multiple directions is extracted by convolution neural network. Then, the information entropy of each sub-band image is calculated and used as the weight of each sub-band image quality. Finally, the quality evaluation values of four sub-band images are weighted and fused to obtain the visual quality values of the entire image. Experimental results show that the proposed method gains advantage from the global and local information of the image, thereby further improving its effectiveness and generalization.

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