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2008
IEEE

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

9 years 4 months ago
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 onedisparity codec.
David M. Chen, David P. Varodayan, Markus Flierl,
Added 30 May 2010
Updated 30 May 2010
Type Conference
Year 2008
Where ICMCS
Authors David M. Chen, David P. Varodayan, Markus Flierl, Bernd Girod
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