Beyond achieving higher compression efficiency over classical image
compression codecs, deep image compression is expected to be improved with
additional side information, e.g., another image from a different perspective
of the same scene. To better utilize the side information under the distributed
compression scenario, the existing method (Ayzik and Avidan 2020) only
implements patch matching at the image domain to solve the parallax problem
caused by the difference in viewing points. However, the patch matching at the
image domain is not robust to the variance of scale, shape, and illumination
caused by the different viewing angles, and can not make full use of the rich
texture information of the side information image. To resolve this issue, we
propose Multi-Scale Feature Domain Patch Matching (MSFDPM) to fully utilizes
side information at the decoder of the distributed image compression model.
Specifically, MSFDPM consists of a side information feature extractor, a
multi-scale feature domain patch matching module, and a multi-scale feature
fusion network. Furthermore, we reuse inter-patch correlation from the shallow
layer to accelerate the patch matching of the deep layer. Finally, we nd that
our patch matching in a multi-scale feature domain further improves compression
rate by about 20% compared with the patch matching method at image domain
(Ayzik and Avidan 2020).

采用多尺度特征域补丁匹配（MSFDPM）模块来充分利用分布式图像压缩模型中的附加信息，从而比图像域中的补丁匹配方法（Ayzik 和 Avidan 2020）更好地解决了视差问题，并提高了约 20% 的压缩率。

特征域多尺度块匹配学习的分布式图像压缩

Learned Distributed Image Compression with Multi-Scale Patch Matching in  Feature Domain

Deep network-based image Compressed Sensing (CS) has attracted much attention
in recent years. However, the existing deep network-based CS schemes either
reconstruct the target image in a block-by-block manner that leads to serious
block artifacts or train the deep network as a black box that brings about
limited insights of image prior knowledge. In this paper, a novel image CS
framework using non-local neural network (NL-CSNet) is proposed, which utilizes
the non-local self-similarity priors with deep network to improve the
reconstruction quality. In the proposed NL-CSNet, two non-local subnetworks are
constructed for utilizing the non-local self-similarity priors in the
measurement domain and the multi-scale feature domain respectively.
Specifically, in the subnetwork of measurement domain, the long-distance
dependencies between the measurements of different image blocks are established
for better initial reconstruction. Analogically, in the subnetwork of
multi-scale feature domain, the affinities between the dense feature
representations are explored in the multi-scale space for deep reconstruction.
Furthermore, a novel loss function is developed to enhance the coupling between
the non-local representations, which also enables an end-to-end training of
NL-CSNet. Extensive experiments manifest that NL-CSNet outperforms existing
state-of-the-art CS methods, while maintaining fast computational speed.

提出了一种使用非局部神经网络（NL-CSNet）的新型图像压缩感知（CS）框架，利用测量域和多尺度特征域中的非局部自相似先验知识，旨在改善重构质量。