ETRI-Knowledge Sharing Plaform



논문 검색
구분 SCI
연도 ~ 키워드


학술대회 Descriptor-based video coding for machine for multi-task
Cited 0 time in scopus Download 0 time Share share facebook twitter linkedin kakaostory
이진영, 이희경, 추현곤, 정원식, 서정일
International Workshop on Advanced Image Technology (IWAIT) 2022 (SPIE 12177), pp.1-5
22HH5900, [전문연구실] 기계를 위한 영상 부호화, 서정일
The coding objective of image and video that are targeted for machine consumption may differ from that for human consumption. For example, machine may only use a part of image or video requested or required by an application whereas human consumption requires whole captured area of image and video. In addition, machine may require grayscale or certain light spectrum, whereas human consumption requires full visible light spectrum. To identify an object of interest, a neural network based image or video analysis task may be performed and the output of a task is an identified feature (latent) and an associated descriptor (inference). Depending on the usage, multiple tasks can be performed in parallel or in series, and as a number of identified feature increases, the chance of feature area overlap increases as well. We propose a pipeline of descriptor based video coding for machine for multi-task. The proposed method is expected to increase coding efficiency when multiple tasks are performed, by minimizing redundant encoding of overlapped area of objects of interest and to increase utilization and re-utilization of features by transmitting inference separately.
KSP 제안 키워드
Coding efficiency, Multiple tasks, Video coding, Visible Light, light spectrum, multi-task, neural network, video analysis