Majid Yaghouti Jafarabad

Papers

Total Citations

H-Index

About

Majid Yaghouti Jafarabad is a researcher whose work lies at the intersection of computer vision, image processing, and 3D data compression. His most-cited contribution, "Depth image compression using geometrical wavelets" (2014), addresses a critical challenge in modern 3D vision: the efficient handling of depth images. As depth sensors become ubiquitous in robotics, autonomous navigation, and emerging fields like free viewpoint and 3D television, the sheer volume of high-resolution, high-frame-rate data demands innovative compression techniques. Jafarabad’s work leverages geometrical wavelets to exploit the inherent redundancy in depth maps, offering a method that preserves structural fidelity while reducing storage and transmission costs. Though his citation count is modest, his research is foundational for applications where bandwidth and processing power are limited. By tackling the practical bottleneck of depth data compression, Jafarabad contributes to making real-time 3D perception more viable—a key enabler for everything from robotic mapping to immersive media. His work reflects a focused effort to bridge algorithmic theory with the pressing hardware constraints of 3D vision systems.