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Adaptive Search Range Decision for Accelerating GPU-based Integer-pel Motion Estimation in HEVC Encoders

HEVC 부호화기에서 GPU 기반 정수화소 움직임 추정을 고속화하기 위한 적응적인 탐색영역 결정 방법

  • Kim, Sangmin (Dept. of Electronic Engineering, Kwangwoon university) ;
  • Lee, Dongkyu (Dept. of Electronic Engineering, Kwangwoon university) ;
  • Sim, Dong-Gyu (Dept. of Computer Engineering, Kwangwoon university) ;
  • Oh, Seoung-Jun (Dept. of Electronic Engineering, Kwangwoon university)
  • Received : 2014.07.10
  • Accepted : 2014.09.04
  • Published : 2014.09.30

Abstract

In this paper, we propose a new Adaptive Search Range (ASR) decision algorithm for accelerating GPU-based Integer-pel Motion Estimation (IME) of High Efficiency Video Coding (HEVC). For deciding the ASR, we classify a frame into two models using Motion Vector Differences (MVDs) then adaptively decide the search ranges of each model. In order to apply the proposed algorithm to the GPU-based ME process, starting points of the ME are decided using only temporal Motion Vectors (MVs). The CPU decides the ASR as well as the starting points and transfers them to the GPU. Then, the GPU performs the integer-pel ME. The proposed algorithm reduces the total encoding time by 37.9% with BD-rate increase of 1.1% and yields 951.2 times faster ME against the CPU-based anchor. In addition, the proposed algorithm achieves the time reduction of 57.5% in the ME running time with the negligible coding loss of 0.6%, compared with the simple GPU-based ME without ASR decision.

본 논문은 High Efficiency Video Coding (HEVC) GPU 기반 정수화소(integer-pel) 움직임 추정(Motion Estimation)을 고속화하기 위한 적응적인 탐색영역 결정 방법을 제안한다. 적응적인 탐색영역은 Motion Vector Difference (MVD)를 이용하여 결정한다. 먼저, 입력 영상의 MVD를 분석하여 입력 영상을 두 모델로 분류한다. 이후 분류된 각 모델의 MVD 특성에 따라 적응적인 탐색영역을 결정한다. 제안하는 알고리즘을 GPU 기반 정수화소 움직임 추정에 적용하기 위해 움직임 추정의 시작점은 이전 프레임의 Motion Vector (MV)로 결정한다. 위 과정은 CPU에서 이뤄지며, CPU는 움직임 추정의 시작점과 적응적인 탐색영역을 GPU에 전송한다. 이후 GPU는 정수화소 움직임 추정을 병렬로 수행한다. 제안하는 알고리즘은 참조 모델 대비 1.1%의 BD-rate 상승과 전체 부호화 시간의 37.9% 감소 및 951.2배 빠른 정수화소 움직임 추정 수행 시간을 얻는다. 또한, 적응적인 탐색영역이 적용되지 않은 단순 병렬화 알고리즘 대비 57.5%의 정수화소 움직임 추정 시간 감소와 0.6% BD-rate 상승을 얻는다.

Keywords

References

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