Research on distortion quality evaluation of computer network shared image based on visual sensitivity DOI Open Access
Jun-Ru Li

International Journal of Wireless and Mobile Computing, Journal Year: 2023, Volume and Issue: 24(1), P. 27 - 27

Published: Jan. 1, 2023

Shared image distortion will affect the user's experience, and then damage people's life entertainment experience.In view of this, this research starts with evaluation classification network shared quality, improves quality algorithm combined sensitive characteristics human vision, verifies its performance superiority through comparative experiments.The results show that some improved reference algorithms reaches highest values, which are 0.7923, 0.3224, 0.7931 0.8213, respectively.The non-reference achieves values positive indicators in comparison 0.487 0.287, respectively, while lowest value negative is 0.902.It can be seen conforms to eyes, has high computational efficiency broad application prospects.

Language: Английский

SEM Image Quality Assessment Based on Intuitive Morphology and Deep Semantic Features DOI Creative Commons
Haoran Wang, Shiyin Li, Jicun Ding

et al.

IEEE Access, Journal Year: 2022, Volume and Issue: 10, P. 111377 - 111388

Published: Jan. 1, 2022

The widespread use of scanning electron microscopy (SEM) has increased the requirements for SEM image quality. images obtained by beam feedback have more complex texture features than natural optical imaging, and this condition results in poor performance algorithms used assessing quality on datasets,meanwhile,the field assessment(IQA) is mostly aimed at specific distortion types. In order to solve above two problems,to address rich texture, few edges, extreme sensitivity degree images, we propose a semantic IQA (TSIQA) method based sparse mask information entropy increase. First, construct neural network containing module (SMM), which extract intuitive spatial channel domains. Simultaneously, growth attention (IGA) introduced into SMM detect difference between current past extracting deep information. assessment experiments datasets show that compared with state-of-the-art methods, including popular no-reference techniques adapted SEM-IQA, TSIQA superiority typical criteria.

Language: Английский

Citations

2

Research on distortion quality evaluation of computer network shared image based on visual sensitivity DOI Open Access
Jun-Ru Li

International Journal of Wireless and Mobile Computing, Journal Year: 2023, Volume and Issue: 24(1), P. 27 - 27

Published: Jan. 1, 2023

Shared image distortion will affect the user's experience, and then damage people's life entertainment experience.In view of this, this research starts with evaluation classification network shared quality, improves quality algorithm combined sensitive characteristics human vision, verifies its performance superiority through comparative experiments.The results show that some improved reference algorithms reaches highest values, which are 0.7923, 0.3224, 0.7931 0.8213, respectively.The non-reference achieves values positive indicators in comparison 0.487 0.287, respectively, while lowest value negative is 0.902.It can be seen conforms to eyes, has high computational efficiency broad application prospects.

Language: Английский

Citations

0