RWZC: A Model-Driven Approach for Learning-based Robust Wyner-Ziv Coding
Journal:
arXiv
Published Date:
Jan 16, 2025
Abstract
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.
