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

Authors

  • Dilip Chaudhary

  • Venkatesh

DOI:

https://doi.org/10.34257/GJCSTDVOL22IS1PG17

Keywords:

deep learning, convolutional neural network (CNN), screen content image (SCI), image quality assessment (IQA), no-reference IQA (NR-IQA)

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

How to Cite

Dilip Chaudhary, & Venkatesh. (2022). Deep CNN Model for Non-Screen Content and Screen Content Image Quality Assessment. Global Journal of Computer Science and Technology, 22(D1), 17–24. https://doi.org/10.34257/GJCSTDVOL22IS1PG17

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

Published

2022-01-22