Base Layer Efficiency in Scalable Human-Machine Coding
Yalda Foroutan Alon Harell Anderson de Andrade Ivan V. Bajić
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.