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In-Band Disparity Compensation for Multiview Image Compression and View Synthesis

This paper presents a novel framework to achieve scalable multiview image compression and view synthesis. The open-loop wavelet-lifting scheme for geometric filtering has been exploited to achieve signal-to-noise ratio scalability and view-type scalability (mono, stereo, or multiview). Spatial scalability is achieved by employing in-band prediction which removes correlations among subbands (level-by-level) via shift-invariant references obtained by overcomplete discrete wavelet transforms. We propose a novel in-band disparity compensated view filtering approach, akin to motion compensated temporal filtering, for achieving a scalable multiview codec. In our codec, hybrid prediction is proposed to deal with occlusions, and a novel cost function in dynamic programming (DP) for disparity estimation is introduced to improve view synthesis quality. Experiments show comparable results at full resolution and significant improvements at coarser resolutions, compared to a conventional spatial prediction scheme. View synthesis efficiency is extensively improved by utilizing disparity estimation from the proposed DP approach.