Wyner-Ziv (WZ) video coding compression efficiency depends critically both on the side information (SI) quality and the correlation noise model (CNM) accuracy. In this context, this paper proposes a learning based decoding approach for transform domain WZ video coding, notably in the context of the following techniques: i) fractional-pixel motion field learning to define the relevance of the SI block candidates, and ii) CNM parameter learning. Experimental results show the proposed learning approach brings consistent RD performance improvements, with coding gains up to 3.9 dB regarding the state-of-the-art DISCOVER WZ video codec for a GOP size of 8.