Low complexity depth mode decision for HEVC-based 3D video coding

Chen, Ming; Yang, Yong; Zhang, Qiuwen; Zhao, Xiaoxin; Gan, Yong

doi:10.1016/j.ijleo.2016.01.204

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…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%

“…Compared with the work in [15], the proposed work achieves a 23.40% time savings on average and it has been implemented in the HTM-8.0 version, whereas the method proposed in the present work was implemented on an HTM-16.3 version, which fully implements the final 3D-HEVC standard. Furthermore, in our proposed algorithm, the coding efficiency loss is better, where the average of BD-BR increase in [15] is approximately 0.58%.…”

Section: Resultsmentioning

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.…”

Section: Introductionmentioning

confidence: 99%

“…While the texture videos and their associated depth map represent the same scene at the same time instant, the CU sizes and prediction modes rarely used in the corresponding texture video are skipped from the associated depth map views to reduce the 3D-HEVC coding complexity. In [15], the authors proposed a low complexity mode decision algorithm for inter and intra prediction using the depth map changes characteristics to skip the unnecessary depth modes for both inter and intra prediction.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bakkouri

Elyousfi

2022

IEEE Access

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“…The new standard is known as 3D video extension of High Efficiency Video Coding (3D-HEVC) [1][2][3]. To improve the multimedia techniques, the Multi-view Video plus Depth maps (MVD) is opted as the best new format for 3D video representation [4]. In the Multi-view Video plus Depth maps format, a video signal is composed of a depth and a texture component.…”

Section: Introductionmentioning

confidence: 99%

Low complexity intra mode decision algorithm for 3D-HEVC

Hamout

Elyousfi

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-The 3D High Efficiency Video Coding (3D-HEVC) has recently been approved by the Joint Collaborative Team on 3D Video coding (JCT-3V) and standardized as an extension of HEVC. To improve the intra coding efficiency of Multi-view Video plus Depth maps (MVD), the 3D-HEVC brings a good intra coding solution, but the computational complexity load increase significantly, which restricts the use of 3D-HEVC encoders in real world application. Therefore, an efficient intra mode decision algorithm is needed. To overcome the aforementioned problem, this paper presents a new mode prediction method enabling high complexity reduction of 3D-HEVC intra coding. The experimental results show that the low complexity intra mode decision algorithm increases the speed of intra coding significantly with negligible loss of encoding efficiency.

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