Encoder-independent decoder-dependent depth-assisted error concealment algorithm for wireless 3D video communication

Three-Dimensional Multi-View Video (3D MVV) contains diverse video streams taken by different cameras around an object. Thence, it is an imperative assignment to fulfill efficient compression to attain future resource bonds whilst preserving a decisive reception MVV quality. The extensive 3D MVV encoding and transmission over mobile or Internet are vulnerable to packet losses on account of the existence of severe channel faults and restricted bandwidth. In this work, we propose a new Encoder-Independent Decoder-Dependent Depth-Assisted Error Concealment (EIDD-DAEC) algorithm. It invests the depth correlations between the temporally, spatially, and inter-view adjoining Macro-Blocks (MBs) to conceal the erroneous streams. At the encoder, the existing inter-view, temporal, and spatial matching are exploited to efficiently compress the 3D MVV streams and to estimate the Disparity Vectors (DVs) and Motion Vectors (MVs). At the decoder, the gathered MVs and DVs from the received coded streams are used to calculate additional depth-assisted MVs and DVs, which are afterwards combined with the collected candidate texture color MVs and DVs groups for concealing the lost MBs of inter- and intra-encoded frames. Finally, the optimum DVs and MVs concealment candidates are selected by the Directional Interpolation Error Concealment Algorithm (DIECA) and Decoder Motion Vector Estimation Algorithm (DMVEA), respectively. Experimental results on several standardized 3D MVV sequences verified the efficacy of the proposed EIDD-DAEC algorithm by achieving ameliorated efficacious objective and subjective results without generating and transporting depth maps at the encoder. The proposed work achieves high 3D MVV quality performance with an improved average Peak Signal-to-Noise Ratio (PSNR) gain by up to 0.95 ~ 2.70 dBs compared to the state-of-the-art error concealment algorithms, which do not employ depth-assisted correlations at different Quantization Parameters (QPs) and Packet Loss Rates (PLRs) of 40%.