Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment

Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment

Dilip Chaudhary

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Venkatesh

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Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment

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Abstract

In the current world, user experience in various platforms matters a lot for different organizations. But providing a better experience can be challenging if the multimedia content on online platforms is having different kinds of distortions which impact the overall experience of the user. There can be various reasons behind distortions such as compression or minimal lighting condition while taking photos. In this work, a deep CNN-based Non-Screen Content and Screen Content NR-IQA framework is proposed which solves this issue in a more effective way. The framework is known as DNSSCIQ. Two different architectures are proposed based upon the input image type whether the input is a screen content or non-screen content image. This work attempts to solve this by evaluating the quality of such images

References

24 Cites in Article

Reference Format

Yuming Li,Lai-Man Po,Xuyuan Xu,Litong Feng,Fang Yuan,Chun-Ho Cheung,Kwok-Wai Cheung (2015). No-reference image quality assessment with shearlet transform and deep neural networks.
A Mittal,A Moorthy,A Bovik (2012). Noreference image quality assessment in the spatial domain.
C Li,A Bovik,X Wu (2011). Blind image quality assessment using a general regression neural network.
A Moorthy,A Bovik (2011). Blind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality.
H Tang,N Joshi,A Kapoor (2011). Learning a blind measure of perceptual image quality.
J Xu,P Ye,D Doermann (2016). Blind image quality assessment based on high order statistics aggregation.
Qiaohong Li,Weisi Lin,Jingtao Xu,Yuming Fang (2016). Blind image quality assessment using statistical structural and luminance features.
Jongyoo Kim,Sanghoon Lee (2017). Deep Learning of Human Visual Sensitivity in Image Quality Assessment Framework.
Yuming Li,Lai-Man Po,Xuyuan Xu,Litong Feng,Fang Yuan,Chun-Ho Cheung,Kwok-Wai Cheung (2015). No-reference image quality assessment with shearlet transform and deep neural networks.
Xialei Liu,Joost Van De Weijer,Andrew Bagdanov (2017). RankIQA: Learning from Rankings for No-Reference Image Quality Assessment.
M Saad,A Bovik,C Charrier (2012). Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain.
K Ma,W Liu,K Zhang,Z Duanmu,Z Wang,W Zuo (2018). End-to-End Blind Image Quality Assessment Using Deep Neural Networks.
Fei Gao,Yi Wang,Panpeng Li,Min Tan,Jun Yu,Yani Zhu,Deepsim (2017). Deep similarity for image quality assessment.
Xiongkuo Min,Guangtao Zhai,Ke Gu,Yutao Liu,Xiaokang Yang (2018). Blind Image Quality Estimation via Distortion Aggravation.
Hossein Talebi,Peyman Milanfar (2018). NIMA: Neural Image Assessment.
W Hou,X Gao,D Tao,X Li (2015). Blind image quality assessment via deep learning.
S Bosse,D Maniry,K U¨ller,T Wiegand,W Samek (2018). Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment.
Dounia Hammou,Sid Fezza,Wassim Hamidouche (2021). EGB: Image Quality Assessment based on Ensemble of Gradient Boosting.
Le Kang,Peng Ye,Yi Li,David Doermann (2015). Simultaneous estimation of image quality and distortion via multi-task convolutional neural networks.
H Sheikh,Z Wang,L Cormack,A Bovik LIVE Image Quality Assessment Database Release 2.
H Sheikh,M Sabir,A Bovik (2006). A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms.
Z Wang,A Bovik,H Sheikh,E Simoncelli (2004). Image quality assessment: from error visibility to structural similarity.
Dinesh Jayaraman,Anish Mittal,Anush Moorthy,Alan Bovik (2012). Objective quality assessment of multiply distorted images.
J Kim,A Nguyen,S Lee (2019). Deep CNN-Based Blind Image Quality Predictor.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dilip Chaudhary. 2026. \u201cDeep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 22 (GJCST Volume 22 Issue D1): .

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GJCST Volume 22 Issue D1
Pg. 17- 24

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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Classification

GJCST-D Classification: F.1.1

Submission ReceivedOctober 6, 2021
Peer Review Double Blind
Handling Editor
Accepted October 26, 2021
Published November 7, 2021

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v1.2

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January 22, 2022

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