2020
DOI: 10.1109/tcsvt.2019.2897403
|View full text |Cite
|
Sign up to set email alerts
|

Depth Sequence Coding With Hierarchical Partitioning and Spatial-Domain Quantization

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 … Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
4
1

Citation Types

0
12
0

Year Published

2021
2021
2022
2022

Publication Types

Select...
4
2
1

Relationship

2
5

Authors

Journals

citations
Cited by 13 publications
(12 citation statements)
references
References 46 publications
(53 reference statements)
0
12
0
Order By: Relevance
“…Peng et al [12] proposed spatial and temporal enhancement filters for the depth discontinuous regions, depth edge regions, and motion regions, which reduce the prediction residual error and coding complexity in mode decision. Since the depth edge is of greater importance, Shahriyar et al [13] proposed a mono-view depth encoder, which preserves edges implicitly by limiting quantization to the spatial-domain. At the same time, the frame-level clustering tendency was exploited with a binary tree based decomposition to achieve higher efficiency in arithmetic coding.…”
Section: A Related Workmentioning
confidence: 99%
“…Peng et al [12] proposed spatial and temporal enhancement filters for the depth discontinuous regions, depth edge regions, and motion regions, which reduce the prediction residual error and coding complexity in mode decision. Since the depth edge is of greater importance, Shahriyar et al [13] proposed a mono-view depth encoder, which preserves edges implicitly by limiting quantization to the spatial-domain. At the same time, the frame-level clustering tendency was exploited with a binary tree based decomposition to achieve higher efficiency in arithmetic coding.…”
Section: A Related Workmentioning
confidence: 99%
“…Next, each cuboid is recursively partitioned by solving the same greedy optimization problem. It was shown that cuboids have excellent global commonality modeling capabilities, are object centric and computationally efficient [12][13][14].…”
Section: Introductionmentioning
confidence: 99%
“…Zhu et al [11] conceived a convolutional neural network (CNN)-based VSO to elevate the coding performance. Shahriyar et al [12] developed an independent depth map encoder with restricted quantization and proposed binary tree-based decomposition.…”
Section: Introductionmentioning
confidence: 99%