Efficient inter-prediction depth coding algorithm based on depth map segmentation for 3D-HEVC

Liao, Yu-Sheng; Chen, Mei-Juan; Yeh, Chia Hung; Lin, Jie; Chen, Chih‐Wei

doi:10.1007/s11042-018-6547-7

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…By comparing with the work in [12], our proposed algorithm reduces greatly the complexity of the 3D-HEVC encoder. It can be seen that the authors do not present the results of all test sequences, and their work achieves only a 16.42% time savings on average in correlation with HTM-15.1, with a 0.33% synthesis BD-BR increase.…”

Section: Resultsmentioning

confidence: 97%

“…The inter-view correlation is used as a priori information to select the optimal prediction unit (PU) and CU sizes. A fast inter prediction algorithm is proposed in [12] using depth map segmentation. The proposed work is based on the CTU classification into uniform and complex CTUs by dividing the depth map into three parts (foreground, middleground, and background) for an early termination of depth map CU encoding.…”

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: 97%

Section: Introductionmentioning

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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“…Ref. [14] proposes a depth-segmentation-enabled complexity reduction algorithm for 3D-HEVC inter-prediction, where the depth map can be classified as background, mid-ground, and foreground. The work in [15] proposes a new entropy-based coding unit size decision algorithm, and the threshold can be decided by maximum similarity technology.…”

Section: Related Workmentioning

confidence: 99%

“…According to the Karush-Kuhn-Tucker (KKT) conditions [30], the parameter Λ in the model p * (s|w) of Equation (11) can be solved by minimizing the dual function ψ(Λ), as in Equations (13) and (14) shown. In this work, the improved iterative scaling (IIS) algorithm is used to solve the optimization problem corresponding to this maximum entropy model [31].…”

Section: The Proposed Approachmentioning

confidence: 99%

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

2020

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Symmetry considerations play a key role in modern science, and any differentiable symmetry of the action of a physical system has a corresponding conservation law. Symmetry may be regarded as reduction of Entropy. This work focuses on reducing the computational complexity of modern video coding standards by using the maximum entropy principle. The high computational complexity of the coding unit (CU) size decision in modern video coding standards is a critical challenge for real-time applications. This problem is solved in a novel approach considering CU termination, skip, and normal decisions as three-class making problems. The maximum entropy model (MEM) is formulated to the CU size decision problem, which can optimize the conditional entropy; the improved iterative scaling (IIS) algorithm is used to solve this optimization problem. The classification features consist of the spatio-temporal information of the CU, including the rate–distortion (RD) cost, coded block flag (CBF), and depth. For the case analysis, the proposed method is based on High Efficiency Video Coding (H.265/HEVC) standards. The experimental results demonstrate that the proposed method can reduce the computational complexity of the H.265/HEVC encoder significantly. Compared with the H.265/HEVC reference model, the proposed method can reduce the average encoding time by 53.27% and 56.36% under low delay and random access configurations, while Bjontegaard Delta Bit Rates (BD-BRs) are 0.72% and 0.93% on average.

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“…In addition, 3D-HEVC adopted several advanced prediction methods for the segment-wise DC coding (SDC) [6], and a depth intra skip mode (DIS) [7], which also cause some complexity. Many fast encoding algorithms were developed to reduce the complexity of the intra coding [8][9][10][11][12][13][14][15] and inter coding [16][17][18] in 3D-HEVC. In particular, there are two research categories to reduce the encoding complexity of the mode decision process in the depth intra coding [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Fast Depth Intra Mode Decision Based on Mode Analysis in 3D Video Coding

Lee

2019

Electronics

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Multiview video plus depth (MVD), which consists of a texture image and its associated depth map, has been introduced as a 3D video format, and 3D video coding, such as 3D-HEVC, was developed to efficiently compress this MVD data. However, this requires high encoding complexity because of the additional depth coding. In particular, intra coding using various prediction modes is very complicated. To reduce the complexity, we propose a fast depth intra mode decision method based on mode analysis. The proposed method adaptively reduces the number of original candidate modes in a mode decision process. Experimental results show that the proposed method achieves high performance in terms of the complexity reduction.

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Efficient inter-prediction depth coding algorithm based on depth map segmentation for 3D-HEVC

Cited by 12 publications

References 26 publications

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

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

Maximum-Entropy-Model-Enabled Complexity Reduction Algorithm in Modern Video Coding Standards

Fast Depth Intra Mode Decision Based on Mode Analysis in 3D Video Coding

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