Base Layer Efficiency in Scalable Human-Machine Coding
This addresses efficiency for video surveillance and traffic monitoring systems where most content is machine-analyzed, but it is incremental as it builds on existing scalable codecs.
The paper tackled the problem of improving base layer coding efficiency in scalable human-machine image codecs, showing gains of 20-40% in BD-Rate for object detection and instance segmentation.
A basic premise in scalable human-machine coding is that the base layer is intended for automated machine analysis and is therefore more compressible than the same content would be for human viewing. Use cases for such coding include video surveillance and traffic monitoring, where the majority of the content will never be seen by humans. Therefore, base layer efficiency is of paramount importance because the system would most frequently operate at the base-layer rate. In this paper, we analyze the coding efficiency of the base layer in a state-of-the-art scalable human-machine image codec, and show that it can be improved. In particular, we demonstrate that gains of 20-40% in BD-Rate compared to the currently best results on object detection and instance segmentation are possible.