English

Depth Sequence Coding with Hierarchical Partitioning and Spatial-domain Quantisation

Information Theory 2018-01-09 v1 math.IT

Abstract

Depth coding in 3D-HEVC for the multiview video plus depth (MVD) architecture (i) deforms object shapes due to block-level edge-approximation; (ii) misses an opportunity for high compressibility at near-lossless quality by failing to exploit strong homogeneity (clustering tendency) in depth syntax, motion vector components, and residuals at frame-level; and (iii) restricts interactivity and limits responsiveness of independent use of depth information for "non-viewing" applications due to texture-depth coding dependency. This paper presents a standalone depth sequence coder, which operates in the lossless to near-lossless quality range while compressing depth data superior to lossy 3D-HEVC. It preserves edges implicitly by limiting quantisation to the spatial-domain and exploits clustering tendency efficiently at frame-level with a novel binary tree based decomposition (BTBD) technique. For mono-view coding of standard MVD test sequences, on average, (i) lossless BTBD achieved ×42.2\times 42.2 compression-ratio and 60.0%-60.0\% coding gain against the pseudo-lossless 3D-HEVC, using the lowest quantisation parameter QP=1QP = 1, and (ii) near-lossless BTBD achieved 79.4%-79.4\% and 6.986.98 dB Bj{\o}ntegaard delta bitrate (BD-BR) and distortion (BD-PSNR), respectively, against 3D-HEVC. In view-synthesis applications, decoded depth maps from BTBD rendered superior quality synthetic-views, compared to 3D-HEVC, with 18.9%-18.9\% depth BD-BR and 0.430.43 dB synthetic-texture BD-PSNR on average.

Keywords

Cite

@article{arxiv.1801.02298,
  title  = {Depth Sequence Coding with Hierarchical Partitioning and Spatial-domain Quantisation},
  author = {Shampa Shahriyar and Manzur Murshed and Mortuza Ali and Manoranjan Paul},
  journal= {arXiv preprint arXiv:1801.02298},
  year   = {2018}
}

Comments

Submitted to IEEE Transactions on Image Processing. 13 pages, 5 figures, and 5 tables

R2 v1 2026-06-22T23:38:52.587Z