Machine learning-based fast CU size decision algorithm for 3D-HEVC inter-coding

Bakkouri, Siham; Elyousfi, Abderrahmane

doi:10.1007/s11554-020-01059-7

Cited by 10 publications

(13 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 6 illustrates more detailed experiment results of the 6 that the proposed algorithm achieves better RD performance for the four test sequences. Table 6 compares the proposed algorithm with the views synthesis performance in related works [10]- [15] that also focus on RA configuration. It can be seen from Table 6 that the proposed algorithm can greatly reduce the encoding complexity compared to the inter-coding related works.…”

Section: Resultsmentioning

confidence: 99%

“…The most promising boosting algorithm is "Adaptive Boosting", namely, AdaBoost, which is introduced by Freund and Schapire [19]. AdaBoost has been applied with large success to manifold benchmark machine learning problems using mainly decision trees as base classifiers [10], [20]. Besides, there is recent evidence that AdaBoost may very well overfit if we combine several hundred thousand classifiers.…”

Section: Boosting Neural Network Algorithmmentioning

confidence: 99%

“…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%

“…Several fast inter coding approaches have been proposed to reduce the complexity of 3D-HEVC encoder [10]- [15]. In our previous work [10], we proposed a fast CU size decision algorithm based on a machine learning algorithm. Thus, The structure tensor is adopted as an extracted feature to build a binary classification model.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, there is still some room left for further complexity reduction. In our previous work [10], we proposed a fast CU size decision algorithm using tensor features as homogeneity determination and a machine learning binary classification model. In this paper, we propose an amelioration of the previous work based on an early termination of the CU encoding algorithm by investigating various features in training sets to reduce the complexity of texture videos and depth maps.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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

Bakkouri

Elyousfi

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Boosting Neural Network Algorithmmentioning

confidence: 99%

Section: A First and Second Order Statistics Features Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Bakkouri

Elyousfi

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

show abstract

VR‐Based improvement of aerobics teaching quality

Jing

2022

Internet Technology Letters

View full text Add to dashboard Cite

Currently, most of students do not pay much attention to aerobics. Affected by various factors, students' participation in aerobics learning is low and lack of interest. However, regular aerobics exercise can enhance physical fitness and improve physical and mental health. Compared with other sports, aerobics is not so boring, and the exercise effect is excellent. The introduction of virtual reality (VR) technology in aerobics teaching not only makes up for the deficiency of traditional aerobics teaching, but also brings students a great sense of novelty, and teachers' teaching efficiency and students' learning efficiency will be significantly improved. The empirical variogram‐based 360° aerobics teaching video coding unit (CU) partition algorithm is proposed to improve coding efficiency and provide real visual experience. Experiments show that the proposed algorithm performs well in BD‐Rate and Quality of Experience (QoE), so that students can adjust movements according to the standard aerobics movements, which will greatly improve the efficiency of aerobics teaching.

show abstract