We propose a data-driven approach to explicitly learn the progressive
encoding of a continuous source, which is successively decoded with increasing
levels of quality and with the aid of correlated side information. This setup
refers to the successive refinement of the Wyner-Ziv coding problem. Assuming
ideal Slepian-Wolf coding, our approach employs recurrent neural networks
(RNNs) to learn layered encoders and decoders for the quadratic Gaussian case.
The models are trained by minimizing a variational bound on the rate-distortion
function of the successively refined Wyner-Ziv coding problem. We demonstrate
that RNNs can explicitly retrieve layered binning solutions akin to scalable
nested quantization. Moreover, the rate-distortion performance of the scheme is
on par with the corresponding monolithic Wyner-Ziv coding approach and is close
to the rate-distortion bound.

我们提出了一种数据驱动的方法来明确学习连续源的渐进编码，该方法通过逐步提高质量并在相关的辅助信息的帮助下进行解码。我们的方法使用递归神经网络来学习二次高斯情况下的分层编码器和解码器，并通过最小化渐进 Wyner-Ziv 编码问题的速率 - 失真函数上的变分界来训练模型。我们证明了递归神经网络能够明确检索与可伸缩嵌套量化类似的分层分组解决方案。此外，该方案的速率 - 失真性能与相应的整体 Wyner-Ziv 编码方法相当，并且接近速率 - 失真界。