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Machine learning-based fast transcoding for low-power video communication in Internet of Things

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Abstract

Aiming at the low-power video communication with low latency at resource-constrained terminals in the Internet of Things, a machine learning-based fast transcoding from distributed video coding (DVC) to high-efficiency video coding (HEVC) is put forward. In order to accelerate the transcoding, the DVC decoding information is efficiently exploited to reduce HEVC encoding complexity at both levels of coding unit (CU) and prediction unit (PU). First of all, the predictions of CU partition and PU modes are regarded as two binary classification tasks. Subsequently, the initial features are extracted from the DVC decoding information and the support vector machine (SVM)-recursive feature elimination algorithm is adopted to select feature vectors to construct the training data which is used to train the SVM classifiers for CU and PU, respectively. By means of the top-down division prediction method, the CU partition is first determined by the trained SVM classifier. For the CUs which are not further split, the PU modes will be predicted to terminate the quad-tree coding process of HEVC in advance, so that HEVC encoding complexity is reduced. Experimental results show that the proposed algorithm can reduce 57.64% computational complexity on average with Bjøntegaard delta bit-rate 2.43%. In terms of transcoding time efficiency, the proposed algorithm outperforms the state-of-the-art fast DVC to HEVC transcoding algorithms based on machine learning.

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Acknowledgements

This work was supported in part by Central Government Funds of Guiding Local Scientific and Technological Development for Sichuan Province under Grant No. 2021ZYD0030, the State Scholarship Fund of China Scholarship Council under Grant No. 202108515045, and the Scientific Research Project of Sichuan Science and Technology Department under Grant No.2021YJ0099.

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Authors and Affiliations

  1. College of Communication Engineering, Chengdu University of Information Technology, Chengdu, 610225, China

    Jia Yang & Ya-Juan Xue

  2. School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Pi-Yan He & Yan-Hua Jin

  3. College of Electronics and Information Engineering, Sichuan University, Chengdu, 610065, China

    Hong Yang

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  1. Jia Yang
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  2. Pi-Yan He
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  3. Hong Yang
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Correspondence to Pi-Yan He.

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Yang, J., He, PY., Yang, H. et al. Machine learning-based fast transcoding for low-power video communication in Internet of Things. SIViP 16, 2183–2191 (2022). https://doi.org/10.1007/s11760-022-02182-7

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