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Parallel implementations of frame rate up-conversion algorithm using OpenCL on heterogeneous computing devices

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Abstract

As a video post-processing technology, frame rate up-conversion (FRUC) converts a low frame rate video into a higher one by inserting intermediate frames between adjacent original frames. Because computing consumption grows rapidly with the increase of video resolution and frame rate, accelerating FRUC by parallel computing may serve as an appropriate method. In this paper, an effective parallel FRUC algorithm is proposed, which consists mainly of two parts: parallel motion estimation algorithm (Three-dimensional Recursive Search algorithm, 3DRS algorithm) and parallel motion compensation algorithm. We design macro-block-level parallelism and candidate motion vector level parallelism strategies based on different granularity in the motion estimation module, and pixel-level parallelism in the motion compensation module. The proposed parallel FRUC algorithm has been tested on different hardware platforms. The results show that the method achieves significant speedups of up to 96× for 1920 × 1080 video and 254× for 3840 × 2160 video when compared with sequential implementation on CPU. Moreover, the OpenCL program of the parallel FRUC algorithm shows good portability on various GPU platforms.

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Acknowledgements

This work is funded in part by the National Natural Science Foundation of China (No. 61303032), the Fundamental Research Funds for the Central Universities (JB160209), and the National Basic Research Program (973 Program) of China (No. 2013CB329402).

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Authors and Affiliations

  1. The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, Xidian University, Xi’an, 710071, China

    Huming Zhu, Duo Wang, Peng Zhang, Zheng Luo, Licheng Jiao & Hong Han

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  1. Huming Zhu
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  2. Duo Wang
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  4. Zheng Luo
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  5. Licheng Jiao
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Correspondence to Huming Zhu.

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Zhu, H., Wang, D., Zhang, P. et al. Parallel implementations of frame rate up-conversion algorithm using OpenCL on heterogeneous computing devices. Multimed Tools Appl 78, 9311–9334 (2019). https://doi.org/10.1007/s11042-018-6532-1

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