Base Layer Efficiency in Scalable Human-Machine Coding

TL;DR

This paper analyzes and improves the efficiency of the base layer in scalable human-machine coding, achieving significant gains in compression for automated analysis tasks like object detection.

Contribution

It demonstrates that the base layer coding efficiency can be enhanced by 20-40% in BD-Rate, surpassing current state-of-the-art results.

Findings

20-40% BD-Rate improvements over existing methods

Enhanced base layer efficiency benefits automated analysis tasks

Potential for more efficient scalable coding in surveillance applications

Abstract

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.