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Bit allocation for multiview image compression using cubic synthesized view distortion model

  • National Institute of Informatics
    ,
  • Ecole Polytechnique Fé;dé;rale de Lausanne
Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Abstract

"Texture-plus-depth" has become a popular coding format for multiview image compression, where a decoder can synthesize images at intermediate viewpoints using encoded texture and depth maps of closest captured view locations via depth-image-based rendering (DIBR). As in other resource-constrained scenarios, limited avail able bits must be optimally distributed among captured texture and depth maps to minimize the expected signal distortion at the decoder. A specific challenge of multiview image compression for DIBR is that the encoder must allocate bits without the knowledge of how many and which specific virtual views will be synthesized at the decoder for viewing. In this paper, we derive a cubic synthesized view distortion model to describe the visual quality of an interpolated view as a function of the view's location. Given the model, one can easily find the virtual view location between two coded views where the maximum synthesized distortion occurs. Using a multi view image codec based on shape-adaptive wavelet transform, we show how optimal bit allocation can be performed to minimize the maximum view synthesis distortion at any intermediate viewpoint. Our experimental results show that the optimal bit allocation can outperform a common uniform bit allocation scheme by up to 1.0dB in coding efficiency performance, while simultaneously being competitive to a state-of-the-art H.264 codec.

Publication Information

Output type

Research Output: Chapter in Book/Report/Conference proceeding Conference contribution Peer-review

Original language

English

Publication milestones

  • Published - 05/09/2011

Publication status

Published - 05/09/2011

Publisher

Institute of Electrical and Electronics Engineers Inc., United States

ISBN (Print)

9781612843490

External Publication IDs

  • handle.net: 10547/292594
  • Scopus: 80155127431

Host publication title

nan

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