Performance Comparison Study of Edge Detection Algorithms in Video Communication

DOI

10.2991/978-94-6463-986-5_53How to use a DOI?

Keywords

Edge Detection Algorithms; Video Communication; HEVC Encoding; Deep Learning

Abstract

With the rapid advancement of video communication technologies and the growing demand for real-time processing in applications such as autonomous driving and industrial automation, efficient edge detection has become increasingly crucial for optimizing video transmission and analysis. Traditional edge detection methods face significant challenges in balancing accuracy, speed, and power consumption, especially when processing high-resolution video streams in resource-constrained environments. This study evaluates edge detection algorithms for video communication optimization, comparing traditional methods, FPGA implementations, and deep learning approaches. Traditional Canny algorithms with FPGA acceleration achieve remarkable 4.628ns latency and 0.257W power consumption, outperforming CPUs by 450,000× in speed. Deep learning-based HED demonstrates superior accuracy (F-score: 0.78) but faces real-time processing challenges. Hybrid methods combining traditional and deep learning techniques show promising balance between accuracy and efficiency. In HEVC encoding applications, edge-guided CTU partitioning reduces encoding time by 41-58% while maintaining quality. The findings provide practical guidance for algorithm selection in autonomous systems and industrial applications, highlighting the need for adaptive solutions in next-generation video processing.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Dinghao Zhang
PY  - 2026
DA  - 2026/02/18
TI  - Performance Comparison Study of Edge Detection Algorithms in Video Communication
BT  - Proceedings of the 2025 International Conference on Electronics, Electrical and Grid Technology (ICEEGT 2025)
PB  - Atlantis Press
SP  - 512
EP  - 521
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-986-5_53
DO  - 10.2991/978-94-6463-986-5_53
ID  - Zhang2026
ER  -