Fast CU size and mode decision algorithm for 3D-HEVC intercoding

Bakkouri, Siham; Elyousfi, Abderrahmane; Hamout, Hamza

doi:10.1007/s11042-019-08461-9

Cited by 11 publications

(6 citation statements)

References 25 publications

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“…Set n c as the column vector. The Mahalanobis distance between two columns is defined in equation (8). n Y in equation ( 7) represents the row vector.…”

Section: Methods For Fast Cu Partition Decisionmentioning

confidence: 99%

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An Early CU Partition Mode Decision Algorithm in VVC Based on Variogram for Virtual Reality 360 Degree Videos

Zhang¹,

Hou²,

Liu³

2021

Preprint

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360-degree videos have become increasingly popular with the development of virtual reality (VR) technology. These videos are converted to a 2D image plane format before being encoded with standard encoders. To improve coding efficiency, a new generation video coding standard has been launched to be known as Versatile Video Coding (VVC). However, the computational complexity of VVC makes it time-consuming to compress 360-degree videos of high resolution. The diversity of CU partitioning modes of VVC greatly increases the computational complexity. Through statistical experiments on ERP videos, it is found that the probability of using horizontal partitioning for such videos is greater than that of vertical partitioning. The empirical variogram combined with Mahalanobis distance is proposed to measure texture orientation information. The experimental results show that the algorithm saves 32.13% of the coding time with only 0.66% BDBR increasing.

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“…Set n c as the column vector. The Mahalanobis distance between two columns is defined in equation (8). n Y in equation ( 7) represents the row vector.…”

Section: Methods For Fast Cu Partition Decisionmentioning

confidence: 99%

“…For HEVC, the rapid determination of CU size is the focus of its research. Based on the structure tensor of each CU, an inter-mode decision algorithm is proposed in [8]. In [9], the time correlation between CU depth and coding parameters are applied to develop a selection model to estimate the range of candidate CU depths.…”

Section: Related Workmentioning

confidence: 99%

An Early CU Partition Mode Decision Algorithm in VVC Based on Variogram for Virtual Reality 360 Degree Videos

Zhang¹,

Hou²,

Liu³

2021

Preprint

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“…3D-HEVC adopts a hierarchical coding structure, which is one of the most powerful tools to improve the coding efficiency of the 3D-HEVC encoder [6], [7]. In the joint model of 3D-HEVC, a complex RD optimization process is performed for all the possible CU sizes to find one with the minimum RD cost and determine the best coding size for a CU.…”

Section: Motivation and Statistical Analysismentioning

confidence: 99%

“…3D-HEVC introduces many techniques and feature tools to further improve coding efficiency. When considering the inter-coding process, 3D-HEVC adopts a hierarchical coding structure which is one of the most powerful tools to improve the coding efficiency of the 3D-HEVC encoder [6], [7]. However, this hierarchical coding structure is based on the quad-tree structure of the Coding Unit (CU).…”

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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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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“…Then, the CU split is early terminated if current CU is in non-ROI region. Bakkouri et al [24] characterize the homogeneity of a CU by calculating the gradient vectors and the structure tensor. Then, a fast algorithm combining early termination of CU split and mode prediction process is accomplished for both the color texture and depth map.…”

Section: Introductionmentioning

confidence: 99%

Fast Texture Coding Based on Spatial, Temporal and Inter-View Correlations for 3D Video Coding

et al. 2021

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3D-HEVC (The 3D Extension of High Efficiency Video Coding), the latest international standard for 3D video coding, supports multiview plus depth 3D video format to enrich multimedia applications. For the texture coding, 3D-HEVC utilizes not only the information of temporal and spatial domains but also that of inter-view domain. However, the time consumption and complexity of 3D-HEVC also increase significantly. In this paper, fast texture coding algorithm for 3D-HEVC is proposed. We individually calculate the Pearson correlation coefficients by the rate-distortion costs (RD-costs) of coding tree units (CTUs) in the temporal, spatial and inter-view domains to analyze the correlations for independent view and dependent view. The proposed coding algorithm is based on the coding information of CTUs with higher correlations. The fast algorithm predicts and dynamically adjusts the depth range of the coding unit (CU). The prediction unit (PU) mode decision is proposed according to the complexity and partition direction of the best PU modes obtained from the highly correlated CTUs. The search range is adaptively adjusted for motion estimation. In addition, the RD-cost threshold is estimated to early terminate the CU split. Experimental results show that the proposed fast texture coding algorithm reduces 40.75% of the texture coding time on average and outperforms numerous previous works significantly.

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Fast CU size and mode decision algorithm for 3D-HEVC intercoding

Cited by 11 publications

References 25 publications

An Early CU Partition Mode Decision Algorithm in VVC Based on Variogram for Virtual Reality 360 Degree Videos

An Early CU Partition Mode Decision Algorithm in VVC Based on Variogram for Virtual Reality 360 Degree Videos

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

Fast Texture Coding Based on Spatial, Temporal and Inter-View Correlations for 3D Video Coding

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