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Massively parallel rate-constrained motion estimation using multiple temporal predictors in HEVC

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Hojati, Esmaeil, Franche, Jean-François, Coulombe, Stéphane et Vázquez, Carlos. 2017. « Massively parallel rate-constrained motion estimation using multiple temporal predictors in HEVC ». In IEEE International Conference on Multimedia and Expo (ICME) (Hong Kong, China, July 10-14, 2017) pp. 43-48. Piscataway, NJ, USA : IEEE.
Compte des citations dans Scopus : 2.

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Abstract

Rate-constrained motion estimation (RCME) is considered to be the most time-consuming process 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 inherent dependencies in the process for deriving motion vector predictors (MVPs) prevent the parallelization of prediction units (PUs) processing. In this paper, we present a framework for performing a two-stage parallel RCME, in which the RCME of all the PUs of a frame can be calculated in parallel. A novel method is introduced to overcome the dependencies inherent to the derivation of MVPs. Multiple temporal predictors (MTPs) within the two-stage parallel RCME framework provide fine-grained parallelism encoding without significant BD-Rate penalty, compared to serial encoding. Experimental results show that our proposed approach achieves a BD-Rate improvement of over 1% as compared to state-of-the-art parallel methods providing similar time reductions.

Item Type: Conference proceeding
Professor:
Professor
Coulombe, Stéphane
Vázquez, Carlos
Affiliation: Génie logiciel et des technologies de l'information, Génie logiciel et des technologies de l'information
Date Deposited: 30 May 2017 20:42
Last Modified: 17 Jan 2020 21:06
URI: https://espace2.etsmtl.ca/id/eprint/15284

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