Perceptual Quality Assessment of Digital Images Using Deep Features

Article Properties
Cite
Ahmed, Nisar, and Hafiz Muhammad Shahzad Asif. “Perceptual Quality Assessment of Digital Images Using Deep Features”. Computing and Informatics, vol. 39, no. 3, 2020, pp. 385-09, https://doi.org/10.31577/cai_2020_3_385.
Ahmed, N., & Asif, H. M. S. (2020). Perceptual Quality Assessment of Digital Images Using Deep Features. Computing and Informatics, 39(3), 385-409. https://doi.org/10.31577/cai_2020_3_385
Ahmed N, Asif HMS. Perceptual Quality Assessment of Digital Images Using Deep Features. Computing and Informatics. 2020;39(3):385-409.
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Citations Analysis
Category Category Repetition
Science: Mathematics: Instruments and machines: Electronic computers. Computer science6
Science: Science (General): Cybernetics: Information theory5
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks5
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics4
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software3
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics: Computer engineering. Computer hardware3
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication2
Technology: Engineering (General). Civil engineering (General)2
Technology: Technology (General): Industrial engineering. Management engineering: Information technology2
Technology: Mechanical engineering and machinery2
Technology: Electrical engineering. Electronics. Nuclear engineering1
Science: Biology (General): Ecology1
Technology: Environmental technology. Sanitary engineering1
The category Science: Mathematics: Instruments and machines: Electronic computers. Computer science 6 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Toward human activity recognition: a survey and was published in 2022. The most recent citation comes from a 2024 study titled Edge-Enhanced TempoFuseNet: A Two-Stream Framework for Intelligent Multiclass Video Anomaly Recognition in 5G and IoT Environments. This article reached its peak citation in 2022, with 5 citations. It has been cited in 9 different journals, 33% of which are open access. Among related journals, the Multimedia Tools and Applications cited this research the most, with 3 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year