A novel feature map compression method based on feature transformation for VCM

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.