Lightweight Motion-Vector-Based Segmentation Mask Tracking for Saliency-Based Rate Control

Abstract

In video encoding, saliency-based rate control improves coding efficiency without compromising perceptual quality by allocating more bits to visually important regions. While object detection can guide bit allocation through rectangular bounding boxes, segmentation masks offer a more accurate delineation of object boundaries. This paper seeks to reduce the significant computational burden of frame-by-frame instance segmentation by proposing a lightweight segmentation mask tracking scheme, in which motion vectors (MVs) from the video encoder are used to predict per-vertex displacements. Altogether, we propose two neural network designs for segmentation mask tracking: (1) a base tracker optimized for tracking accuracy; and (2) a lite tracker that balances accuracy and computational complexity. Our experimental results show that the base tracker attains 70-88% of the accuracy of frame-by-frame instance segmentation but achieves a 48× speedup and reduces computational complexity to 0.03% on CPU. For the lite tracker, the corresponding figures are 49×, 0.01%, and 67-88%. Despite tradeoffs in tracking accuracy, reducing complexity to a fraction makes our solution a viable option for practical applications.