Image-Feature Parallel Compression for Indoor Surveillance Video

Intelligent applications such as metaverse built on IoT technology are usually inseparable from the collection of surveillance video data. Yet, how to effectively compress indoor surveillance video has always been a huge challenge for data transmission. In this paper, we propose an image-feature parallel compression framework for indoor surveillance video. In other words, the video is divided into images and features which are compressed separately. Specifically, according to the Mean-shift algorithm, the background image is generated, encoded and shared among frames. Based on a series of detection and matching algorithms, a human feature (HF) extractor is designed to extract a frontage body image, identity features, boundary features and structural features which are used for the generation of foreground body images. At the same time, body masks are generated in order to segment body areas from body images. Finally, with the guidance of body mask and boundary features, the body area covers the corresponding area of generated background image to reconstruct the video, and its quality is improved by a quality enhancement network. Experimental results show that the proposed compression scheme achieves coding gain up to 66.91% and 51.84% averagely compared with HEVC and VVC respectively.