Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)

A Fast Depth Intra Mode Selection Algorithm

Authors
Jieling Fan, Qiang Li, Jianlin Song
Corresponding Author
Jieling Fan
Available Online November 2016.
DOI
10.2991/aiie-16.2016.26How to use a DOI?
Keywords
depth intra coding; depth modeling mode; wedgelet pattern
Abstract

For the purpose of reducing the complexity of depth intra coding, a fast intra prediction mode decision algorithm is proposed. First, the fast decision of depth modeling modes (DMMs) is obtained by using Laplace Operator detection method. Second by using the correlation between the wedgelet pattern and texture features, only the wedgelet patterns, which are related to the intra angle mode, is traversed. As a consequence, the number of wedgelet patterns is reduced through this algorithm. Experiment results indicate that the proposed algorithm can reach the same goal with the reduction of computational complexity by 24.7%, while the quality of the video is almost unchanged.

Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
Series
Advances in Intelligent Systems Research
Publication Date
November 2016
ISBN
978-94-6252-271-8
ISSN
1951-6851
DOI
10.2991/aiie-16.2016.26How to use a DOI?
Copyright
© 2016, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jieling Fan
AU  - Qiang Li
AU  - Jianlin Song
PY  - 2016/11
DA  - 2016/11
TI  - A Fast Depth Intra Mode Selection Algorithm
BT  - Proceedings of the 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016)
PB  - Atlantis Press
SP  - 105
EP  - 109
SN  - 1951-6851
UR  - https://doi.org/10.2991/aiie-16.2016.26
DO  - 10.2991/aiie-16.2016.26
ID  - Fan2016/11
ER  -