Thriving underwater applications demand efficient extreme compression
technology to realize the transmission of underwater images (UWIs) in very
narrow underwater bandwidth. However, existing image compression methods
achieve inferior performance on UWIs because they do not consider the
characteristics of UWIs: (1) Multifarious underwater styles of color shift and
distance-dependent clarity, caused by the unique underwater physical imaging;
(2) Massive redundancy between different UWIs, caused by the fact that
different UWIs contain several common ocean objects, which have plenty of
similarities in structures and semantics. To remove redundancy among UWIs, we
first construct an exhaustive underwater multi-scale feature dictionary to
provide coarse-to-fine reference features for UWI compression. Subsequently, an
extreme UWI compression network with reference to the feature dictionary
(RFD-ECNet) is creatively proposed, which utilizes feature match and reference
feature variant to significantly remove redundancy among UWIs. To align the
multifarious underwater styles and improve the accuracy of feature match, an
underwater style normalized block (USNB) is proposed, which utilizes underwater
physical priors extracted from the underwater physical imaging model to
normalize the underwater styles of dictionary features toward the input.
Moreover, a reference feature variant module (RFVM) is designed to adaptively
morph the reference features, improving the similarity between the reference
and input features. Experimental results on four UWI datasets show that our
RFD-ECNet is the first work that achieves a significant BD-rate saving of 31%
over the most advanced VVC.

提出了一种创新的水下图像压缩网络（RFD-ECNet），利用特征匹配和参考特征变体大幅减少水下图像之间的冗余，并通过使用提取自水下物理成像模型的水下物理先验来规范化字典特征的水下风格，从而对齐多样化的水下风格并提高特征匹配的准确性，实验结果显示与最先进的 VVC 相比，RFD-ECNet 在四个水下图像数据集上实现了显著的 BD 率节省 31%。