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A hardware-friendly algorithm for LCU-level pipe-lined integer motion estimation

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Abstract

The latest video coding standards such as AVS3 adopts numerous novel coding tools and new partition mechanisms to achieve a better coding performance. But they also massively increase the computation overhead. Integer motion estimation (IME) is one of the most important and complex coding tools. The growing complexity of these video coding standards brings up severe challenges for both software and hardware implemented IME. Different from the software oriented IME algorithms, hardware-friendly IME algorithms often require low data dependency, regular data access and reasonable trade-off among compression performance, latency and resources. The challenges also highly relate to the hardware encoder architecture applying the IME algorithm. We propose a novel hardware-friendly integer motion estimation algorithm for AVS3 targeted for the Largest Coding Unit (LCU) level pipe-lined encoder architectures. The proposed algorithm consists of three parts, namely, a predictive motion vector prediction (PMVP) algorithm to derive predictive motion vectors (PMV) in advance to avoid the data dependency, a multi-resolution hierarchical motion estimation method that generates motion vectors (MVs) for 60% of the divided coding units (CUs) and a motion vector inference (MVI) method to infer MVs for the rest of the CUs. The proposed algorithms reduce the computation complexity by 88.64% for IME and only suffers 0.11% performance degradation for the IPPP configuration.

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Data availability statement

With intellectual property concerns, our implementation of the algorithms is not open sourced. But a demo consisting of an executable file is available on our project https://github.com/zhuxihehe/HW_LCU_IME_FOR_AVS3. The test results can be validated with the executable file under the described conditions.

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Authors and Affiliations

  1. School of Computer Science, Peking University, Beijing, China

    Xizhong Zhu, Guoqing Xiang & Xiaodong Xie

  2. Advanced Institute of Information Technology, Peking University, Hangzhou, China

    Xizhong Zhu, Guoqing Xiang & Peng Zhang

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  1. Xizhong Zhu
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  2. Guoqing Xiang
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Correspondence to Xizhong Zhu.

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Xizhong Zhu, Guoqing Xiang, Peng Zhang and Xiaodong Xie contributed equally to this work.

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Zhu, X., Xiang, G., Zhang, P. et al. A hardware-friendly algorithm for LCU-level pipe-lined integer motion estimation. Multimed Tools Appl 83, 6685–6709 (2024). https://doi.org/10.1007/s11042-023-15669-3

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  • DOI: https://doi.org/10.1007/s11042-023-15669-3

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