Wyner-Ziv coding of multiview images with unsupervised learning of two disparities

Wyner-Ziv coding of multiview images is an attractive solution because it avoids communications between individual cameras. To achieve good rate-distortion performance, the Wyner-Ziv decoder must reliably estimate the disparities between the multiview images. For the scenario where two reference images exist at the decoder, we propose a codec that effectively performs unsupervised learning of the two disparities between an image being Wyner-Ziv coded and the two reference images. The proposed two-disparity decoder disparity-compensates the two references images and generates side information more accurately than an existing one-disparity decoder. Experimental results with real multiview images demonstrate that the proposed codec achieves PSNR gains of 1-5 dB over the one-disparity codec.