Fast Prediction Algorithm in High Efficiency Video Coding Intra-mode Based on Deep Feature Learning

Kebin JIA; Tenghe CUI; Pengyu LIU; Chang LIU

doi:10.11999/JEIT200414

Volume 43 Issue 7

Jul. 2021

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Kebin JIA, Tenghe CUI, Pengyu LIU, Chang LIU. Fast Prediction Algorithm in High Efficiency Video Coding Intra-mode Based on Deep Feature Learning[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2023-2031. doi: 10.11999/JEIT200414

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Kebin JIA, Tenghe CUI, Pengyu LIU, Chang LIU. Fast Prediction Algorithm in High Efficiency Video Coding Intra-mode Based on Deep Feature Learning[J]. Journal of Electronics & Information Technology, 2021, 43(7): 2023-2031. doi: 10.11999/JEIT200414

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Fast Prediction Algorithm in High Efficiency Video Coding Intra-mode Based on Deep Feature Learning

doi: 10.11999/JEIT200414

1.
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
2.
Beijing Laboratory of Advanced Information Networks, Beijing 100124, China

Funds: The National Natural Science Foundation of China (61672064), The National Key Research and Development Project of China (2018YFF01010100), The Basic Research Program of Qinghai Province (2020-ZJ-709)

Received Date: 2020-05-26
Rev Recd Date: 2020-12-15

Available Online: 2021-01-05

Publish Date: 2021-07-10

Abstract

Abstract

Compared to H.264/AVC coding standard, High Efficiency Video Coding (HEVC) improves the compression efficiency, but the consequent disadvantage is the significant increase in encoding complexity by using the quad-tree partition. A Multi-Layer Feature Transfer Convolutional Neural Network (MLFT-CNN) for Coding Unit (CU) division and characterization vector prediction in HEVC intra coding mode is proposed, which greatly reduces the complexity of video coding. Firstly, a reduced-resolution feature extraction module incorporating CU partition structure information is proposed. Then, the channel attention mechanism is improved for a better texture expression performance of the feature. After that, the feature transfer mechanism is designed to use the feature division of high-depth coding unit to guide the division of low-depth coding unit. Finally, the target loss function represented by the segmented feature is established, and the end-to-end CU division represents the vector prediction network. The experimental results show that the proposed algorithm effectively reduces the encoding complexity of HEVC without affecting the video coding quality. Specifically, compared to the standard method, the encoding complexity on the standard test sequence is reduced by 70.96% on average.
- High Efficiency Video Coding(HEVC),
- Complexity reduction,
- Deep learning,
- Intra coding

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References(20)

References

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