Practical real-time image compression for resource-challenged devices

Kevin Pham; Arthur C. Depoian II; Colleen P. Bailey

doi:10.1117/12.3013994

7 June 2024 Practical real-time image compression for resource-challenged devices

Kevin Pham, Arthur C. Depoian II, Colleen P. Bailey

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 130330F (2024) https://doi.org/10.1117/12.3013994
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

In the era of rapidly expanding image data, the demand for improved image compression algorithms has grown significantly, particularly with the integration of deep learning approaches into traditional image processing tasks. However, many of the existing solutions in this domain are burdened by computational complexity, rendering them unsuitable for real-time deployment on standard devices as they often necessitate complex systems and substantial energy consumption. This work addresses the growing paradigm of edge computing for real-time applications by introducing a novel, on-edge device solution. This innovative approach aims to strike a balance between efficiency and accuracy, adhering to the practical constraints of real-world deployment. By presenting demonstrations of the proposed solution’s performance on readily available devices, we provide tangible evidence of its applicability and viability in real-world scenarios. This advance contributes to the ongoing dialogue about the need for accessible and efficient image compression algorithms that can be deployed real-time applications on edge devices, bridging the gap between the demanding computational requirements of deep learning and the practical limitations of everyday hardware. As data continues to surge, solutions like this become ever more critical in ensuring effective image compression, aligning with on-edge computing within AI. This research paves the way for improved image processing in real-time applications while conserving computational resources and energy consumption.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kevin Pham, Arthur C. Depoian II, and Colleen P. Bailey "Practical real-time image compression for resource-challenged devices", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 130330F (7 June 2024); https://doi.org/10.1117/12.3013994

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