Deep Feature Compression with Rate-Distortion Optimization for Networked Camera Systems

Deep-learning-based video analysis solutions have become indispensable components in today's intelligent sensing applications. In a networked camera system, an efficient way to analyze the captured videos is to extract the features for deep learning at local cameras or edge devices and then transmit the features to powerful processing hubs for further analysis. As there exists substantial redundancy among different feature maps from the same video frame, the feature maps could be compressed before transmission to save bandwidth. This paper introduces a new rate-distortion optimized framework for compressing the intermediate deep features from the key frames of a video. First, to reduce the redundancy among different features, a feature selection strategy is designed based on hierarchical clustering. The selected features are then quantized, repacked as videos, and further compressed using a standardized video encoder. Furthermore, the proposed framework incorporates rate-distortion models that are built for three representative computer vision tasks: image classification, image segmentation, and image retrieval. A corresponding rate-distortion optimization module is designed to enhance the performance of common computer vision tasks under rate constraints. Experimental results show that the proposed deep feature compression framework can boost the compression performance over the standard HEVC video encoder.