Compression of thermal images for machine vision based on objectness measure

Shin Kim; Yegi Lee; Hanshin Lim; Hyon-Gon Choo; Jeongil Seo; Kyoungro Yoon

doi:10.1117/12.2626066

30 April 2022 Compression of thermal images for machine vision based on objectness measure

Shin Kim, Yegi Lee, Hanshin Lim, Hyon-Gon Choo, Jeongil Seo, Kyoungro Yoon

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121773C (2022) https://doi.org/10.1117/12.2626066
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

Recent development of intelligent object detection systems requires high-definition images for reliable detection accuracy performance, which can cause a high occupation problem of network bandwidth as well as archiving storage capacity. In this paper, we propose an objectness measure-based image compression method of thermal images for machine vision. Based on the objectness of a certain area, bounding box for the area with high objectness is adjusted in order not to affect the possible object detection performance and the image is compressed in a way that the area having a high objectness is compressed with lower compression ratio than other area. The experiments indicate that superior object detection accuracy at comparable BPP is accomplished using the proposed scheme to that of the state-of-the-art video compression method.

Citation Download Citation

Shin Kim, Yegi Lee, Hanshin Lim, Hyon-Gon Choo, Jeongil Seo, and Kyoungro Yoon "Compression of thermal images for machine vision based on objectness measure", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121773C (30 April 2022); https://doi.org/10.1117/12.2626066

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