  Display technology must offer high dynamic range (HDR) contrast-based depth
induction and 3D personalization simultaneously. Efficient algorithms to
compress HDR stereo data is critical. Direct capturing of HDR content is
complicated due to the high expense and scarcity of HDR cameras. The HDR 3D
images could be generated in low-cost by fusing low-dynamic-range (LDR) images
acquired using a stereo camera with various exposure settings. In this paper,
an efficient scheme for coding multi-exposure stereo images is proposed based
on a tensor low-rank approximation scheme. The multi-exposure fusion can be
realized to generate HDR stereo output at the decoder for increased realism and
exaggerated binocular 3D depth cues.
  For exploiting spatial redundancy in LDR stereo images, the stack of
multi-exposure stereo images is decomposed into a set of projection matrices
and a core tensor following an alternating least squares Tucker decomposition
model. The compact, low-rank representation of the scene, thus, generated is
further processed by 3D extension of High Efficiency Video Coding standard. The
encoding with 3D-HEVC enhance the proposed scheme efficiency by exploiting
intra-frame, inter-view and the inter-component redundancies in low-rank
approximated representation. We consider constant luminance property of IPT and
Y'CbCr color space to precisely approximate intensity prediction and
perceptually minimize the encoding distortion. Besides, the proposed scheme
gives flexibility to adjust the bitrate of tensor latent components by changing
the rank of core tensor and its quantization. Extensive experiments on natural
scenes demonstrate that the proposed scheme outperforms state-of-the-art
JPEG-XT and 3D-HEVC range coding standards.
