Priors Guided Extreme Underwater Image Compression for Machine Vision and Human Vision

Abstract

A low bit-rate compression is required for underwater images due to the limited bandwidth in underwater acoustic communication, which limits performances of both machine analysis and human perception in most underwater applications. Few existing compression methods consider the unique characteristics of underwater images such as color shift and haze effect to better fulfill requirements of various applications under low bit-rates. To address this problem, we propose a novel extreme underwater image compression framework, which can provide scalability to support machine vision and human vision with the assistance of underwater priors. Specifically, the base layer is composed of a feature extractor and a generator, where global structural edges and high-level features of regions with a significant impact on machine analysis are extracted and used for reconstructing a feature-matching image for analysis purpose. Considering the negative influence of underwater imaging processes on machine vision, in this article, a feature degradation removal module guided by underwater priors is proposed to alleviate feature-level degradation via taking analysis-friendly enhanced images as auxiliary information. As for the enhancement layer aiming for human vision, the residual between the original image and the reconstruction from base layer is compressed. A feature attention block and a background light recovery block are designed utilizing features extracted from enhanced images and the underwater before further recovering the original scene with a good perception quality under low bit-rates. Experimental results demonstrate the superiority of our framework in both machine vision tasks and perception quality compared with traditional compression methods and learned-based methods.