Descriptor-based video coding for machine for multi-task

Jin Young Lee; HeeKyung Lee; Hyon-Gon Choo; Won-Sik Cheong; Jeongil Seo

doi:10.1117/12.2626039

30 April 2022 Descriptor-based video coding for machine for multi-task

Jin Young Lee, HeeKyung Lee, Hyon-Gon Choo, Won-Sik Cheong, Jeongil Seo

Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 121773D (2022) https://doi.org/10.1117/12.2626039
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China

Abstract

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.

Citation Download Citation

Jin Young Lee, HeeKyung Lee, Hyon-Gon Choo, Won-Sik Cheong, and Jeongil Seo "Descriptor-based video coding for machine for multi-task", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 121773D (30 April 2022); https://doi.org/10.1117/12.2626039

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