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Multi-Pass and Frame Parallel Algorithms of Motion Estimation in H.264/AVC for Generic GPU

Authors: Chuan-Yiu Lee; Yu-Cheng Lin; Chi-Ling Wu; Chin-Hsiang Chang; You-Ming Tsao; Shao-Yi Chien;

Multi-Pass and Frame Parallel Algorithms of Motion Estimation in H.264/AVC for Generic GPU

Abstract

In this paper, multi-pass and frame parallel algorithms are proposed to accelerate various motion estimation (ME) tools in H.264 with the graphics processing unit (GPU). By the multi-pass method to unroll and rearrange the multiple nested loops, the integer-pel ME can be implemented with two-pass process on GPU. Moreover, fractional ME needs six passes for frame interpolation with six-tap filter and motion vector refinement. Motion estimation with multiple reference frames can be implemented with two-pass process with frame-level parallel scheme by use of SIMD vector operations of GPU. Experimental results show that, compared to implementations with only CPU, about 6 times to 56 times speed-up can be achieved for different ME algorithms.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
25
Average
Top 10%
Average