In this paper, we consider the one-shot version of the classical Wyner-Ziv problem where a source is compressed in a lossy fashion when only the decoder has access to a correlated side information. Following the entropy-constrained quantization framework, we assume a scalar quantizer followed by variable length entropy coding. We consider compression of a uniform source, motivated by its role in the compression of processes with low-dimensional features embedded within a high-dimensional ambient space. We find upper and lower bounds to the entropy-distortion functions of the uniform source for quantized and noisy side information, and illustrate tightness of the bounds at high compression rates.

翻译：本文考虑经典Wyner-Ziv问题的一次性版本，其中信源在有损方式下进行压缩，仅解码器可获取相关边信息。遵循熵约束量化框架，我们假设采用标量量化器后接变长熵编码。受均匀信源在高维环境空间中嵌入低维特征过程的压缩作用启发，我们研究均匀信源的压缩问题。针对量化与含噪边信息场景，给出了均匀信源熵失真函数的上下界，并论证了高压缩率下边界的紧致性。