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Fast HEVC intra-CU decision partition algorithm with modified LeNet-5 and AlexNet

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Abstract

The High-Efficiency Video Coding (HEVC) standard has emerged to achieve high coding efficiency performance while introducing several novel tools. One contributor key to the performance gain over Advanced Video Coding (AVC) H.264 is the unit partition structure that extended a large number of coding unit shapes (CU) ranging from 64\(\times \)64 to 8x8 to replace the macroblock structure of H.264/AVC. This pioneering module achieves a significant gap of compression performance at the expense of additional encoding complexity, which increases under HEVC All-intra configuration due to the use of the Rate-Distortion Optimization (RDO) process. Since I-frame significantly affects the coding efficiency, the main goal of the proposed work is to implement Convolutional Neural Network-based approaches to substitute the brute force RDO search without affecting the compression efficiency performance. LeNet-5-based and AlexNet-based approaches are developed to eliminate extensive computational time used to check all block decision candidates, which deeply optimize the HEVC coding unit partition module for All-intra configuration. In the first step, a database was created for the HEVC intra-mode to learn different models. Subsequently, modified LeNet-5 (M-LeNet-5) and modified AlexNet (M-AlexNet) models are implemented to predict the HEVC CU partition and their performances are compared. Experimental results indicated that the proposed algorithms could speed up the CU partition structure by reducing the intra-mode encoding time up to 85% and 75% with M-LeNet-5 and M-Alex-Net, respectively.

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Authors and Affiliations

  1. Electronic and Information Technology Laboratory, University of Sfax, Sfax, Tunisia

    Werda Imen, Belghith Fatma & Nouri Masmoudi

  2. Electronics and Microelectronics Laboratory, University of Monastir, Monastir, Tunisia

    Maraoui Amna

  3. Networked objects, control and communication systems, University of Sousse, Sousse, Tunisia

    Sayadi Fatma Ezahra

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  1. Werda Imen
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  2. Maraoui Amna
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  5. Nouri Masmoudi
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Imen, W., Amna, M., Fatma, B. et al. Fast HEVC intra-CU decision partition algorithm with modified LeNet-5 and AlexNet. SIViP 16, 1811–1819 (2022). https://doi.org/10.1007/s11760-022-02139-w

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  • DOI: https://doi.org/10.1007/s11760-022-02139-w

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