Saliency-Driven Versatile Video Coding for Neural Object Detection

Abstract

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.

arXiv comment5 pages, 3 figures, 2 tables; Originally submitted at IEEE ICASSP 2021

References in corpus (2)

• Video Coding for Machines with Feature-Based Rate-Distortion Optimization
• On Intra Video Coding and In-loop Filtering for Neural Object Detection Networks

Cited by in corpus (4)

• Boosting Neural Image Compression for Machines Using Latent Space Masking
• Evaluation of Video Coding for Machines without Ground Truth
• Learning Frequency-Specific Quantization Scaling in VVC for Standard-Compliant Task-driven Image Coding
• OneAdapt: Fast Configuration Adaptation for Video Analytics Applications via Backpropagation