Muhammad Irshad

About Me

Muhammad Irshad is an accomplished researcher with expertise in the field of Computer Science. He obtained his B.Sc degree in Computer Science from Bahauddin Zakariya University, Multan, in 2009, and his M.S. degree from Air University Islamabad, in 2015. Recently, he completed his Ph.D. in Electronic Engineering and System Automation program at University of Brasília, Brazil in 2022. Muhammad's research focuses on developing innovative solutions for improving the quality of multimedia content using the image and video quality assessment, perceptual quality assessment, underwater image enhancement, multimedia content, and machine learning techniques. His expertise and contributions to the field make him a valuable asset to any organization needing cutting-edge research and innovation. More specifically, my research interests include deep learning, quality assessment, feature extraction, visual attention, and underwater image quality metric.

Prior to the PhD, I obtained a Bachelor's degree in computer science from Bahaudding Zakariya University, Multan, Pakistan. I also obtained a master of science in computer science from Air University, Islamabad, Pakistan.

Publications

Journals

CNN-based no-reference video quality assessment method using a spatiotemporal saliency patch selection procedure - [code]

Sana Alamgeer,Muhammad Irshad, and Mylène C. Q. Farias.

Journal of Electronic Imaging - SPIE (2021)

Conferences

Light field image quality assessment method based on deep graph convolutional neural network: research proposal

Sana Alamgeer, Muhammad Irshad, and Mylène C. Q. Farias

MMSys '22: Proceedings of the 13th ACM Multimedia Systems Conference (2022)

No-reference Image Quality Assessment of Underwater Images Using Multi-Scale Salient Local Binary Patterns

Muhammad Irshad, Camilo Sanchez-Ferreira, Sana Alamgeer, Carlos H. Llanos, and Mylène C. Q. Farias

Conference of IS&T International Symposium on Electronic Imaging (2021)

Perceptual quality assessment of enhanced images using a crowd-sourcing framework

Muhammad Irshad, Alessandro Silva, Sana Alamgeer, and Mylène C. Q. Farias

Conference of IS&T International Symposium on Electronic Imaging (2020)

Projects

Deep learning-based Visual Attention model for 360-degree videos

The goal of this project is to incorporate the aspects of visual attention into the design of bottom-up saliency prediction models using deep learning for 360-degree videos.