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

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Dilip Chaudhary
Dilip Chaudhary
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Venkatesh
Venkatesh

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GJCST Volume 22 Issue D1
Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment

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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

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.

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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Advanced deep CNN model for non-screen content quality assessment.
Issue Cover
GJCST Volume 22 Issue D1
Pg. 17- 24
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-D Classification: F.1.1
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v1.2

Issue date

January 22, 2022

Language

English

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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

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

Dilip Chaudhary
Dilip Chaudhary
Venkatesh
Venkatesh

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