Optimizing Stereo-to-Multiview Conversion for Autostereoscopic Displays


We present a novel stereo-to-multiview video conversion method for glasses-free multiview displays.

April 7, 2014
Eurographics 2014



Alexandre Chapiro (Disney Research/ETH Joint PhD)

Simon Heinzle (Disney Research)

Tunç Aydın (Disney Research)

Steven Poulakos (Disney Research)

Matthias Zwicker (University of Bern)

Aljoscha Smolic (Disney Research)

Markus Gross (Disney Research/ETH Zurich)

Optimizing Stereo-to-Multiview Conversion for Autostereoscopic Displays


Different from previous stereo-to-multiview approaches, our mapping algorithm utilizes the limited depth range of autostereoscopic displays optimally and strives to preserve the scene’s artistic composition and perceived depth even under strong depth compression. We first present an investigation of how subjective perceived image quality relates to spatial frequency and disparity. The outcome of this study is utilized in a two-step mapping algorithm, where we (i) compress the scene depth using a non-linear global function to the depth range of an autostereoscopic display, and (ii) enhance the depth gradients of salient objects to restore the perceived depth and salient scene structure. Finally, an adapted image domain warping algorithm is proposed to generate the multiview output, which enables overall disparity range extension.

Copyright Notice