Quality Metrics and Efficient Compression Techniques for Immersive Plenoptic Video Content

Description

The proposed research aims to develop innovative quality metrics and efficient compression techniques for immersive plenoptic video content, a technology that captures detailed light field and depth data for more realistic viewing experiences. Plenoptic videos, with applications in virtual reality (VR), augmented reality (AR), telemedicine, and immersive entertainment, pose significant challenges due to their large data requirements, necessitating advanced compression methods without compromising quality. The project focuses on two key areas: creating new quality metrics that reflect depth perception and multi-angle views to enhance user experience, and designing compression algorithms to reduce the required bandwidth while preserving visual fidelity. The latest compression standards and deep learning-based models will be adapted for plenoptic content, ensuring seamless transmission and rendering even in bandwidth-limited environments. The outcomes will improve the quality and accessibility of immersive experiences, driving advancements in digital media, healthcare, and communication technologies.

Researchers

• Maissan Bazazeh
• Nusrat Mehajabin

Selected Publications

• Joseph Khoury, Nusrat Mehajabin, Mahsa Pourazad, Panos Nasiopoulos, V. C.M. Leung. An efficient three-dimensional prediction structure for coding light field video content using the MV-HEVC standard. International Journal of Multimedia Intelligence and Security, 2022.
• Nusrat Mehajabin, Mahsa Pourazad, Panos Nasiopoulos. An Efficient Pseudo-Sequence-Based Light Field Video Coding Utilizing View Similarities for Prediction Structure. IEEE Transactions on Circuits and Systems for Video Technology, 2022.
• Maissan Bazazeh, Hamid Reza Tohidypour, Junbin Zhang, Panos Nasiopoulos, Z. Yu, Y. Meng, G. Li, A. Singh, Abrar Wafa. An Efficient Compression Strategy for Light Field Video Data Management. MMEDIA, 2024.
• Tala Bazzaza, Maissan Bazazeh, M. Rashidinia, W. Qian, Hamid Reza Tohidypour, Panos Nasiopoulos. Enhancing Light Field Video Compression Efficiency via View Selection and Synthesis Techniques. INTELLI, 2024.
• Nusrat Mehajabin, Tala Bazzaza, Hamid Reza Tohidypour, Yixiao Wang, Panos Nasiopoulos. Light Field Video Compression Efficiency Through View Omission and Synthesis. CVMP, 2023.
• Yixiao Wang, Nusrat Mehajabin, Hamid Reza Tohidypour, J. Song, M. Huang, B. Babaghorbani, Z. Chen, Mahsa Pourazad, Panos Nasiopoulos, V.C.M. Leung. A Spatial Calibrated and Colour Corrected Light Field Outdoor Video Dataset from a 5x5 Dense Camera Array. ISCAS, 2023.