Effective CU size decision algorithm based on depth map homogeneity for 3D-HEVC inter-coding

Bakkouri, Siham; Elyousfi, Abderrahmane

doi:10.1109/iscv49265.2020.9204037

Cited by 4 publications

(1 citation statement)

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“…The precision of the CU size decision in a classification task is highly dependent on the feature space used to train the model. In most of the machine learning algorithms adopted in CU size decision of 3D-video coding, the features extracted from a CU are always statistic criteria, such as variance, structure tensor and gradient [7], [10], [29], [30]. In this paper, we are training two types of features measure.…”

Section: A First and Second Order Statistics Features Extractionmentioning

confidence: 99%

Early Termination of CU Partition Based on Boosting Neural Network for 3D-HEVC Inter-Coding

Bakkouri

Elyousfi

2022

IEEE Access

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As an extension of the High Efficiency Video Coding (HEVC) standard, the 3D-HEVC needs to encode multiple texture videos and depth maps components. In the 3D-HEVC inter-coding test model, a large variety of Coding Unit (CU) sizes are adopted to select the one with the lowest Rate-Distortion (RD) cost as the best CU size. This technique provides the highest achievable coding efficiency, but it brings a huge computational complexity which limits 3D-HEVC from practical applications. In this paper, early termination of CU encoding is proposed to reduce the complexity caused by the CU size splitting process. The proposed algorithm is based on CU homogeneity and a boosting neural network clustering algorithm. The algorithm contained three main steps. The first step is for the extraction of various features from the original encoder. Then, the selection of the features, which had a high correlation with CU partition using a machine learning algorithm. In the second step, a boosting neural network model is used for training the selected features to derive the threshold values for our proposed algorithm. In the final step, an efficient early termination of CU splitting is released for texture videos and depth maps based on the extracted thresholds from the training model. The experimental results show that the proposed algorithm reduces a significant encoding time, while the loss in coding efficiency is negligible.

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