Hind Kanj
Papers
1
Total Citations
2
H-Index
1
About
Hind Kanj is a researcher focused on the critical challenge of minimizing latency in real-time interactive video systems. Her work primarily addresses the Glass-to-Glass (G2G) delay—the total time from video capture to display—which is essential for applications like teleoperated driving, remote robot control, and telepresence. In her most-cited paper, "Glass-to-Glass Delay Reduction: Encoding Rate Reduction vs. Video Frame Extrapolation" (2023), she systematically compares two key strategies for reducing this delay: lowering encoding bitrate versus using predictive frame extrapolation. This contribution provides a practical framework for optimizing quality of experience in latency-sensitive scenarios. While her citation count is still growing, Kanj’s research is directly relevant to the advancement of immersive remote operations and autonomous systems. Her work stands out for its focus on a measurable, user-centric metric—G2G delay—rather than abstract network performance, making her findings valuable for engineers designing next-generation real-time video pipelines. As the demand for low-latency interaction increases, Kanj’s contributions are poised to have lasting impact on both academic research and industrial applications.
Research Focus
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Top Papers
- 1