Paper
25 March 2023 A novel feature map compression method based on feature transformation for VCM
Author Affiliations +
Proceedings Volume 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023; 125921H (2023) https://doi.org/10.1117/12.2666465
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2023, 2023, Jeju, Korea, Republic of
Abstract
In this paper, we propose a novel feature map compression method for Video Coding for Machines (VCM). The proposed method performs a principal component analysis (PCA)-based transform on feature pyramid network (FPN) feature maps using predefined basis and mean vectors. In addition, the proposed method reduces redundancy between different resolution levels within FPN feature maps based on redundancy between FPN layers. The fixed predefined basis and mean are employed through PCA with a set of training data set. For any input videos, transform coefficients are obtained by performing transform with the fixed basis and compressed using Versatile Video Coding (VVC). Experimental results show that the proposed method achieves 89.22% and 86.57% BD-rate gain compared to the VCM feature anchor in instance segmentation, and object detection, respectively.
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Minhun Lee, Seungjin Park, Seoung-Jun Oh, Younhee Kim, Se Yoon Jeong, and Donggyu Sim "A novel feature map compression method based on feature transformation for VCM", Proc. SPIE 12592, International Workshop on Advanced Imaging Technology (IWAIT) 2023, 125921H (25 March 2023); https://doi.org/10.1117/12.2666465
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KEYWORDS
Video coding

Principal component analysis

Video

Object detection

Video compression

Video surveillance

Feature extraction

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