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arXiv 2501.09520cs.ITmath.IT
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RWZC:一种面向基于学习的鲁棒Wyner-Ziv编码的模型驱动方法

RWZC: A Model-Driven Approach for Learning-based Robust Wyner-Ziv Coding

Yuxuan Shi, Shuo Shao, Yongpeng Wu, Wenjun Zhang, Merouane Debbah

AI总结:

本文提出模型驱动的鲁棒Wyner-Ziv编码框架RWZC,通过建模图像间仿射关系实现可学习掩码生成与速率自适应联合信源信道编码,在非平稳相关真实样本上相比现有方法取得显著性能提升。

摘要:

本文考虑了一种新颖的基于学习的Wyner-Ziv编码框架,应用于分布式图像传输场景,其中相关源仅在接收端可用。与其他可学习框架不同,我们的方法对非平稳源相关性具有鲁棒性,即图像对之间的重叠信息会发生变化。具体而言,我们首先对相关图像之间的仿射关系进行建模,并利用该模型进行可学习掩码生成和速率自适应联合信源信道编码。此外,我们还提供了一个变形预测网络,以消除信道干扰和仿射变换带来的失真。直观上,所观察到的性能提升主要源于关注简单的几何关系,而非源之间复杂的联合分布。数值结果表明,在应用于具有非平稳相关性的真实世界样本时,与最先进方法相比,我们的框架在PSNR上获得了1.5 dB的增益,在MS-SSIM上获得了0.2的提升,并在感知指标上具有显著优势。

英文摘要:

In this paper, a novel learning-based Wyner-Ziv coding framework is considered under a distributed image transmission scenario, where the correlated source is only available at the receiver. Unlike other learnable frameworks, our approach demonstrates robustness to non-stationary source correlation, where the overlapping information between image pairs varies. Specifically, we first model the affine relationship between correlated images and leverage this model for learnable mask generation and rate-adaptive joint source-channel coding. Moreover, we also provide a warping-prediction network to remove the distortion from channel interference and affine transform. Intuitively, the observed performance improvement is largely due to focusing on the simple geometric relationship, rather than the complex joint distribution between the sources. Numerical results show that our framework achieves a 1.5 dB gain in PSNR and a 0.2 improvement in MS-SSIM, along with a significant superiority in perceptual metrics, compared to state-of-the-art methods when applied to real-world samples with non-stationary correlations.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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