Sorry, you need to enable JavaScript to visit this website.

HIGHLY PARALLEL HEVC MOTION ESTIMATION BASED ON MULTIPLE TEMPORAL PREDICTORS AND NESTED DIAMOND SEARCH

Primary tabs

Citation Author(s):
Esmaeil Hojati, Jean-François Franche, Stéphane Coulombe, Carlos Vázquez
Submitted by:
Stephane Coulombe
Last updated:
26 September 2017 - 8:28am
Document Type:
Poster
Document Year:
2017
Event:
Presenters Name:
Stéphane Coulombe
Paper Code:
MQ-PC.5

Abstract 

Abstract: 

Rate-constrained motion estimation (RCME) is the most computationally intensive task of H.265/HEVC encoding. Massively parallel architectures, such as graphics processing units (GPUs), used in combination with a multi-core central processing unit (CPU), provide a promising computing platform to achieve fast encoding. However, the dependencies in deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing at a frame level. Moreover, the conditional execution structure of typical fast search algorithms is not suitable for GPUs designed for data-intensive parallel problems. In this paper, we propose a novel highly parallel RCME method based on multiple temporal motion vector (MV) predictors and a new fast nested diamond search (NDS) algorithm well-suited for a GPU. The proposed framework provides fine-grained encoding parallelism. Experimental results show that our approach provides reduced GPU load with better BD-Rate compared to prior full search parallel methods based on a single MV predictor.

up
0 users have voted:

Comments

This is the poster that was presented to ICIP2017.

This is the poster that was presented at the ICIP 2017.

This is the poster that was presented at the ICIP 2017.

Dataset Files

ICIP2017-Poster-Hojati.pdf

(699)