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학술대회 Compression of Thermal Images for Machine Vision based on Objectness Measure
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김신, 이예지, 임한신, 추현곤, 서정일, 윤경로
International Workshop on Advanced Image Technology (IWAIT) 2022 (SPIE 12177), pp.1-5
21HH4800, [전문연구실] 기계를 위한 영상 부호화, 서정일
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 제안 키워드
Accuracy performance, Bounding Box, Compression method, Detection accuracy, High definition, Image Compression, Intrusion detection system(IDS), Network bandwidth, Object detection, Thermal image, Video compression