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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.