Intermediate deep feature coding for human–machine vision collaboration

Abstract

Traditional image coding are mainly designed for human vision. While for collaborative intelligence, deep feature coding is specific for machine vision, which includes feature extraction and compression. Actually, deep features can build a bridge between human and machine vision. Therefore, we focus on generalized deep feature extraction and compression for multitask, which includes image reconstruction task for human vision and computer visual tasks for machine vision. After analyzing correlation among multitask, a reconstruction guided feature extraction strategy and feature fusion based network are proposed to get more generalized intermediate deep feature, which contains sufficient information friendly for human and machine vision. Besides, a non-uniform quantization method based on importance and a compact representation method for feature distribution information protection are proposed for high efficiency feature coding. Eventually, we come up with an entire intermediate deep feature coding framework including feature extraction and compression. Experimental results indicate the performance gains with our framework.