Extreme Underwater Image Compression Using Physical Priors

Underwater images (UWIs) require higher compression ratio than terrestrial images due to the limited bandwidth of underwater wireless acoustic channel. In many studies such as marine species, the foreground objects (FGOs) in UWIs need to be observed in detail, while the background only needs to be viewed in general. However, existing image compression methods achieve limited compression ratio and reconstruction quality, which cannot fulfill these practical applications since they do not consider the unique underwater physical priors. To overcome the limitation, we propose an underwater physical prior-based extreme compression network (PPECN) for UWIs compression, which includes an underwater physical prior-guided FGOs autoencoder (UPGAE) and a FGOs-assisted background generator (FG-BGGAN). Specifically, we design an underwater physical prior guidance structure that simulates the data flow in the underwater physical imaging process to adaptively adjust the distribution of received Gaussian features in the UPGAE to be more consistent with real UWIs. During the adjustment, some basic UWI properties can be reconstructed, which can improve the reconstruction quality and implicitly reduce bits through the end-to-end training. Furthermore, the background is generated from simple semantic map under the constraint of the perceptual consistency between background and FGOs, significantly saving coding bits and improving the perceptual quality of the generated background. Extensive experimental results on four underwater image datasets verify that, compared with state-of-the-art compression methods, our PPECN achieves both impressive improvement in the perceptual quality of the whole image and significant gain in the pixel fidelity of the FGOs at the similar low bitrate.