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Conference Paper
Deep learning-based Feature Compression for Video Coding for Machine
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- Authors
- Jihoon Do, Jooyoung Lee, Younhee Kim, Se Yoon Jeong, Jin Soo Choi
- Issue Date
- 2022-01
- Citation
- International Workshop on Advanced Image Technology (IWAIT) 2022 (SPIE 12177), pp.1-5
- Publisher
- SPIE
- Language
- English
- Type
- Conference Paper
- Abstract
- We previously trained the compression network via optimization of bit-rate and distortion (feature domain MSE) [1]. In this paper, we propose feature map compression method for Video Coding for Machine (VCM) based on deep learning-based compression network that joint training for optimizing both compressed bit rate and machine vision task performance. We use bmshij2018-hyperporior model in the CompressAI [2] as the compression network and compress the feature map which is the output of stem layer in the Faster R-CNN X101-FPN network of Detectron2 [3]. We evaluated the proposed method by Evaluation Framework for MPEG VCM. The proposed method shows the better results than VVC of MPEG VCM Anchor.
- KSP Keywords
- Bit Rate, Compression method, Feature Map, Feature compression, Learning-based, Video coding, deep learning(DL), evaluation framework, faster R-CNN, joint training, machine vision