  Saliency-driven image and video coding for humans has gained importance in
the recent past. In this paper, we propose such a saliency-driven coding
framework for the video coding for machines task using the latest video coding
standard Versatile Video Coding (VVC). To determine the salient regions before
encoding, we employ the real-time-capable object detection network You Only
Look Once~(YOLO) in combination with a novel decision criterion. To measure the
coding quality for a machine, the state-of-the-art object segmentation network
Mask R-CNN was applied to the decoded frame. From extensive simulations we find
that, compared to the reference VVC with a constant quality, up to 29 % of
bitrate can be saved with the same detection accuracy at the decoder side by
applying the proposed saliency-driven framework. Besides, we compare YOLO
against other, more traditional saliency detection methods.
