Small Object-Aware Video Coding for Machines via Feature-Motion Synergy

Video coding for machines (VCM) is a rapidly growing field dedicated to bridging the gap between video and feature coding. For storage-intensive aerial videos, VCM offers valuable insights into a more efficient coding paradigm. However, the frequent occurrence of small objects poses a challenge to VCM, with limited distinctive features and inherent distortion in the reconstructed videos. To address this issue, we propose small object-aware VCM (SOAVCM), a joint video and feature coding approach that handles small objects. Particularly, the video coding incorporates a feature-guided residual (FGR) codec to preserve the small objects, utilizing features obtained from feature coding. Simultaneously, feature coding employs the motion vector (MV) estimated in video coding to generate compact high-level features. By leveraging the inherent synergy between features and MVs, SOAVCM significantly enhances overall coding efficiency. Experimental results demonstrate that SOAVCM outperforms several deep-learning-based methods and traditional coding standards in video coding. Moreover, the encoded feature representation improves detection accuracy and achieves substantial bitrate savings.