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Detail
Conference Paper
Compression of Thermal Images for Machine Vision based on Objectness Measure
- Authors
- Shin Kim, Yegi Lee, Hanshin Lim, Hyon-Gon Choo, Jeongil Seo, Kyoungro Yoon
- Issue Date
- 2022-01
- Citation
- International Workshop on Advanced Image Technology (IWAIT) 2022 (SPIE 12177), pp.1-5
- Language
- English
- Type
- Conference Paper
- Project Code
- 21HH4800, Video Coding for Machine, Jeongil Seo
- 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.
- KSP Keywords
- Accuracy performance, Bounding Box, Compression method, Detection accuracy, High definition, Image Compression, Intrusion detection system(IDS), Network bandwidth, Object detection, Thermal image, Video compression