Abstract
Multi-view image compression (MIC) aims to achieve high compression efficiency by exploiting inter-image correlations, playing a crucial role in 3D applications. As a subfield of MIC, distributed multi-view image compression (DMIC) offers performance comparable to MIC while eliminating the need for inter-view information at the encoder side. However, existing methods in DMIC typically treat all images equally, overlooking the varying degrees of correlation between different views during decoding, which leads to suboptimal coding performance. To address this limitation, we propose a novel $\textbf{OmniParallax Attention Mechanism}$ (OPAM), which is a general mechanism for explicitly modeling correlations and aligned features between arbitrary pairs of information sources. Building upon OPAM, we propose a Parallax Multi Information Fusion Module (PMIFM) to adaptively integrate information from different sources. PMIFM is incorporated into both the joint decoder and the entropy model to construct our end-to-end DMIC framework, $\textbf{ParaHydra}$. Extensive experiments demonstrate that $\textbf{ParaHydra}$ is $\textbf{the first DMIC method}$ to significantly surpass state-of-the-art MIC codecs, while maintaining low computational overhead. Performance gains become more pronounced as the number of input views increases. Compared with LDMIC, $\textbf{ParaHydra}$ achieves bitrate savings of $\textbf{19.72%}$ on WildTrack(3) and up to $\textbf{24.18%}$ on WildTrack(6), while significantly improving coding efficiency (as much as $\textbf{65}\times$ in decoding and $\textbf{34}\times$ in encoding).
Abstract (translated)
多视角图像压缩（MIC）旨在通过利用图像间的相关性来实现高效压缩，在三维应用中扮演着重要角色。作为MIC的一个子领域，分布式多视角图像压缩（DMIC）在性能上可与MIC相媲美，同时消除了编码端对视图间信息的需求。然而，现有的DMIC方法通常认为所有图像同等重要，忽略了在解码过程中不同视图之间相关性的差异性，导致了次优的编码效果。 为解决这一限制，我们提出了一种新颖的**全方位偏移注意力机制（OmniParallax Attention Mechanism, OPAM）**，这是一种通用机制，用于明确建模任意信息源对之间的相关性和对齐特征。基于OPAM，我们提出了一个**视差多信息融合模块（PMIFM）**，以自适应地整合来自不同来源的信息。我们将PMIFM集成到联合解码器和熵模型中，构建了我们的端到端DMIC框架——**ParaHydra**。 广泛的实验表明，**ParaHydra**是第一个显著超越最新MIC编解码器性能的DMIC方法，并且保持了较低的计算开销。随着输入视图数量的增加，性能提升更加明显。与LDMIC相比，在WildTrack(3)上，**ParaHydra**实现了19.72%的比特率节省，在WildTrack(6)上最多可达到24.18%，同时大幅提高了编码效率（解码速度最高提升了65倍，编码速度提升高达34倍）。
URL
https://arxiv.org/abs/2603.03615
