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Volume 41 Issue 11
Nov.  2019
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Yang ZHOU, Jiayi WU, Yu LU, Haibing YIN. Depth Map Error Concealment for 3D High Efficiency Video Coding[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2760-2767. doi: 10.11999/JEIT180926
Citation: Yang ZHOU, Jiayi WU, Yu LU, Haibing YIN. Depth Map Error Concealment for 3D High Efficiency Video Coding[J]. Journal of Electronics & Information Technology, 2019, 41(11): 2760-2767. doi: 10.11999/JEIT180926

Depth Map Error Concealment for 3D High Efficiency Video Coding

doi: 10.11999/JEIT180926
Funds:  Zhejiang Province Natural Science Fundation (LY17F020027), The National Natural Science Foundation of China (61401132, 61572449)
  • Received Date: 2018-09-30
  • Rev Recd Date: 2019-04-05
  • Available Online: 2019-05-21
  • Publish Date: 2019-11-01
  • By using the intra-view and inter-view correlations and the motion vector-sharing, a depth map error concealment approach is proposed for 3D video coding based on the High Efficiency Video Coding (3D-HEVC) to combat the packet loss of the depth video transmission. Based on the Hierarchical B-frame Prediction (HBP) structure in 3D-HEVC and textured features of the depth map, all the lost coding units are firstly categorized into two classes, i.e., motion blocks and static blocks. Then, according to the outer boundary matching criterion combining the texture structure, the optimal motion/disparity vector is chosen for the damaged motion blocks to conduct the motion/disparity compensation based error concealment. Whereas, the direct copy is applied to concealling the damaged static blocks quickly. Finally, for the concealed blocks whose qualities are not ideal, the new motion/disparity compensation blocks reconstructed by the reference frames recombination are applied to improning the qualities of those blocks. The experimental results show that the repaired depth map concealed by the proposed approach can achieve 0.25~2.03 dB gain in term of the Peak-Signal-to-Noise Ratio (PSNR) and 0.001~0.006 gain in term of Structural Similarity Index Measure(SSIM). Moreover, the subjective visual quality of the repaired area is better in lines with the original depth maps.
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