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.

翻译：我们提出了一种数据驱动方法，用于显式学习连续源的渐进编码，该编码在相关边信息的辅助下以逐步提升的质量水平进行解码。该设置对应于Wyner-Ziv编码问题的渐进细化。在理想Slepian-Wolf编码的假设下，我们的方法采用循环神经网络（RNN）来学习二次高斯情形下的分层编码器与解码器。模型通过最小化渐进细化Wyner-Ziv编码问题率失真函数的变分界进行训练。我们证明RNN能够显式恢复类似于可扩展嵌套量化的分层装箱解。此外，该方案的率失真性能与相应的整体式Wyner-Ziv编码方法相当，且接近率失真界。