MambaSIC：基于 Mamba 与双向多参考熵模型的立体图像压缩
MambaSIC: Mamba-based Stereo Image Compression with Bi-directional Multi-reference Entropy Model
用 Mamba 状态空间块替代交叉注意力来建模立体视图间的冗余，并用棋盘格分区的双向多参考熵模型替代空间自回归，为视觉压缩类任务提供了「线性复杂度 + 并行熵编码」的可复用范式，可直接迁移到视频压缩、光场压缩等多视图场景。
原文依据 we propose a Mamba-based stereo visual state space block (stereo VSSB) that leverages its linear complexity and long-range modeling capabilities
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论随着立体图像压缩（SIC）在三维重建与自主导航等领域的应用迅速增长，其重要性日益凸显。已有方法利用交叉注意力建模视图间冗余，并采用自回归熵模型预测概率分布，取得了令人瞩目的率失真性能。然而，交叉注意力机制的二次复杂度以及熵模型的空间自回归迭代导致其编码速度缓慢。为解决上述局限，我们提出 MambaSIC，包含两项关键创新。首先，我们提出基于 Mamba 的立体视觉状态空间块（stereo VSSB），利用其线性复杂度与长程建模能力，更快速、更高效地捕获两个视图之间的冗余信息。其次，为加速压缩过程并提升概率估计的准确性，我们引入双向多参考熵模型，该模型利用棋盘格分区策略与 stereo VSSB 获取丰富的视图间先验。实验结果表明，MambaSIC 在压缩性能与效率两方面均优于最优方法。
英文原文摘要 1253 字符
Stereo image compression (SIC) has become increasingly vital with its applications surging in fields such as 3D reconstruction and autonomous navigation. Previous methods leverage cross-attention to model inter-view redundancy and employ autoregressive entropy models to predict probability distributions, achieving impressive rate-distortion performance. However, they suffer from slow coding speed due to the quadratic complexity of cross-attention mechanisms and the spatial autoregressive iterations of the entropy models. To address these limitations, we propose MambaSIC, which introduces two key innovations. First, we propose a Mamba-based stereo visual state space block (stereo VSSB) that leverages its linear complexity and long-range modeling capabilities to more rapidly and efficiently capture redundancy information between the two views. Second, to accelerate the compression process and enhance the accuracy of probability estimation, we introduce a bi-directional multi-reference entropy model that utilizes a checkerboard partitioning strategy and the stereo VSSB to get rich inter-view priors. Experimental results demonstrate that our MambaSIC outperforms the state-of-the-art methods in both compression performance and efficiency.