Depth Sequence Coding with Hierarchical Partitioning and Spatial-domain Quantization

Shampa Shahriyar, Manzur Murshed, Mortuza Ali, Manoranjan Paul

Research output: Contribution to journalArticle

Abstract

Depth coding in 3D-HEVC deforms object shapes due to block-level edge-approximation and lacks efficient techniques to exploit the statistical redundancy, due to the framelevel clustering tendency in depth data, for higher coding gain at near-lossless quality. This paper presents a standalone monoview depth sequence coder, which preserves edges implicitly by limiting quantisation to the spatial-domain and exploits the frame-level clustering tendency efficiently with a novel binary tree based decomposition (BTBD) technique. BTBD can exploit the statistical redundancy in frame-level syntax, motion components, and residuals efficiently with fewer block-level prediction/coding modes and simpler context modelling for context-adaptive arithmetic coding. Compared to the depth coder in 3D-HEVC, the proposed one has achieved significantly lower bitrate at lossless to near-lossless quality range for mono-view coding and rendered superior quality synthetic views from the depth maps, compressed at the same bitrate, and the corresponding texture frames.
Original languageEnglish
Pages (from-to)835-849
Number of pages15
JournalIEEE Transactions on Circuits and Systems for Video Technology
Volume30
Issue number3
Early online date04 Feb 2019
DOIs
Publication statusPublished - Mar 2020

Grant Number

  • DP130103670

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