FreqSIC：频率感知的双向棋盘上下文立体图像压缩
FreqSIC: Frequency-aware Stereo Image Compression with Bi-directional Checkerboard Context Model
把立体图像压缩中的跨视角冗余建模拆到高/低频分量上分别处理，并用棋盘式非自回归上下文替代逐点自回归熵模型，可直接复用到需要低延迟熵编码的立体匹配、双目深度与光场压缩管线中。
原文依据 we replace the conventional autoregressive framework with a checkerboard strategy and integrate the FSCT module to model inter-view priors
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论立体图像压缩对广泛的 3D 视觉应用至关重要。近期方法通过空间域立体变换与先进的自治回归熵模型，在消除跨视角冗余和实现紧凑熵编码方面展现出强大能力。然而，这些方法往往存在高频信息损失，并带来可观的编码延迟。为克服上述局限，我们提出一种新的频率立体上下文传递（FSCT）模块。与空间域方法不同，FSCT 模块分别在高频与低频分量上捕获跨视角冗余，并动态平衡二者的贡献以保持重建质量。此外，我们用棋盘策略替代传统的自治回归框架，并集成 FSCT 模块来建模跨视角先验，从而实现更快、更高效的熵编码。大量实验表明，我们的方法在现有立体图像压缩方法中取得了最优的率失真性能，同时实现了最低的编码延迟。
英文原文摘要 1109 字符
Stereo image compression is essential for a wide range of 3D vision. Recent methods have demonstrated strong capabilities in eliminating inter-view redundancy and enabling compact entropy coding via spatial-domain stereo transformation and advanced autoregressive entropy models. However, these approaches often suffer from high-frequency information loss and incur considerable coding latency. To overcome these limitations, we propose a novel frequency stereo context transfer (FSCT) module. Unlike spatial-domain methods, the FSCT module separately captures inter-view redundancy in high- and low-frequency components and dynamically balances their contributions to preserve reconstruction quality. In addition, we replace the conventional autoregressive framework with a checkerboard strategy and integrate the FSCT module to model inter-view priors, enabling faster and more efficient entropy coding. Extensive experiments demonstrate that our method achieves state-of-the-art rate-distortion performance among existing stereo image compression approaches, while also attaining the lowest coding latency.