Inter-Feature-Map Differential Coding of Surveillance Video
This addresses bandwidth saving for surveillance video processing in edge-cloud systems, but it is incremental as it extends known methods to video inputs.
The paper tackles compressing intermediate feature maps in collaborative intelligence for surveillance videos, achieving a compression ratio comparable to or better than HEVC with small accuracy reduction.
In Collaborative Intelligence, a deep neural network (DNN) is partitioned and deployed at the edge and the cloud for bandwidth saving and system optimization. When a model input is an image, it has been confirmed that the intermediate feature map, the output from the edge, can be smaller than the input data size. However, its effectiveness has not been reported when the input is a video. In this study, we propose a method to compress the feature map of surveillance videos by applying inter-feature-map differential coding (IFMDC). IFMDC shows a compression ratio comparable to, or better than, HEVC to the input video in the case of small accuracy reduction. Our method is especially effective for videos that are sensitive to image quality degradation when HEVC is applied