Abstract: The Scalable High Efficiency Video Coding (SHVC) has been proposed to improve the coding efficiency. However, this additional extension generally results an important coding complexity. Several studies were performed to overcome the complexity through algorithmic optimizations that led to an encoding time reduction. In fact, mode decision analysis is imperatively important in order to have an idea about the partitioning modes based on two parameters, such as prediction unit size and frame type. This paper presents statistical observations at two levels: coding units (CUs) and prediction units (PUs) selected by the encoder. Analysis was performed for several test sequences with different motion and texture characteristics. The experimental results show that the percentage of choosing coding or prediction unit size and type depends on sequence parameters, frame type, and temporal level.

Ibtissem Wali, Amina Kessentini, Mohamed Ali Ben Ayed and Nouri Masmoudi, “Depth Partitioning and Coding Mode Selection Statistical Analysis for SHVC” International Journal of Advanced Computer Science and Applications(IJACSA), 8(1), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080124

@article{Wali2017,
title = {Depth Partitioning and Coding Mode Selection Statistical Analysis for SHVC},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080124},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080124},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {1},
author = {Ibtissem Wali and Amina Kessentini and Mohamed Ali Ben Ayed and Nouri Masmoudi}
}