CNN-LSTM Learning Approach-Based Complexity Reduction for High-Efficiency Video Coding Standard

High-Efficiency Video Coding provides a better compression ratio compared to earlier standard, H.264/Advanced Video Coding. In fact, HEVC saves 50% bit rate compared to H.264/AVC for the same subjective quality. This improvement is notably obtained through the hierarchical quadtree structured Coding...

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Bibliographic Details
Published in:Scientific programming Vol. 2021; pp. 1 - 10
Main Authors: Bouaafia, Soulef, Khemiri, Randa, Maraoui, Amna, Sayadi, Fatma Elzahra
Format: Journal Article
Language:English
Published: New York Hindawi 25.03.2021
John Wiley & Sons, Inc
Subjects:
Algorithms
Coding
Coding standards
Complexity
Compression ratio
Deep learning
Efficiency
Heuristic
Machine learning
Methods
Multimedia
Optimization
Partitions
Reduction
Structural hierarchy
Video compression
ISSN:1058-9244, 1875-919X
Online Access:Get full text
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Summary:High-Efficiency Video Coding provides a better compression ratio compared to earlier standard, H.264/Advanced Video Coding. In fact, HEVC saves 50% bit rate compared to H.264/AVC for the same subjective quality. This improvement is notably obtained through the hierarchical quadtree structured Coding Unit. However, the computational complexity significantly increases due to the full search Rate-Distortion Optimization, which allows reaching the optimal Coding Tree Unit partition. Despite the many speedup algorithms developed in the literature, the HEVC encoding complexity still remains a crucial problem in video coding field. Towards this goal, we propose in this paper a deep learning model-based fast mode decision algorithm for HEVC intermode. Firstly, we provide a deep insight overview of the proposed CNN-LSTM, which plays a kernel and pivotal role in this contribution, thus predicting the CU splitting and reducing the HEVC encoding complexity. Secondly, a large training and inference dataset for HEVC intercoding was investigated to train and test the proposed deep framework. Based on this framework, the temporal correlation of the CU partition for each video frame is solved by the LSTM network. Numerical results prove that the proposed CNN-LSTM scheme reduces the encoding complexity by 58.60% with an increase in the BD rate of 1.78% and a decrease in the BD-PSNR of -0.053 dB. Compared to the related works, the proposed scheme has achieved a best compromise between RD performance and complexity reduction, as proven by experimental results.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2021/6628041