Efficient multiview video plus depth coding for 3D-HEVC based on complexity classification of the treeblock

Zhang, Qiuwen; Huang, Kunqiang; Xiao, Wang; Jiang, Bin; Gan, Yong

doi:10.1007/s11554-017-0692-5

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…In addition to HTM, the proposed overall method is also compared with state-of-the-art works for an objective comparison. These algorithms include FDML [32], FDTFD [33], FDTC [35], OLCR [37], FMDR [38], FMDG [40], and FMCC [41], which are the fast and efficient algorithms for 3D-HEVC. Note that, the FDTC and OLCR method are designed for texture coding only in 3D-HEVC; the FDML and FDTFD method are designed for depth coding only in 3D-HEVC.…”

Section: Results Of Overall Methodsmentioning

confidence: 99%

“…To evaluate the performance of proposed overall method, the proposed fast scheme is implemented on recent 3D-HEVC encoder (HTM 16.1 [40]). We have tested eight MVD sequences in two resolutions (1024×768 and 1920×1088) recommended by JCT-3V Group from common test condition (CTC) [41]. Each test MVD sequence contains three views.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…A fast mode decision method is introduced in [40] to save the coding time of 3D-HEVC by using the gradient information. In our previous work [41], a fast MVD coding scheme including fast motion and mode size selection is presented for 3D-HEVC, where about 60.1% complexity reduction is achieved with only 0.57% BD-rate loss. The aforementioned methods can effectively accelerate the encoding time of 3D-HEVC and maintain almost the same video quality with original encoder.…”

Section: Introductionmentioning

confidence: 99%

“…View similarities exist in the texture and depth image contents (instant and view point at the same time), which contains strong correlation. In our previous works [41] and [42], we skipped some unnecessary modes in 3D-HEVC to reduce the coding complexity only according to the correlations of temporal-spatial and inter-view. As a result, we cannot achieve the optimum coding performance, which has a relatively low the correlation of texture-depth.…”

Section: Introductionmentioning

confidence: 99%

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A Fast and Efficient 3D-HEVC Method for Complexity Reduction Based on the Correlations of Inter-View, Spatio-Temporal, and Texture-Depth

et al. 2020

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3D High Efficiency Video Coding (3D-HEVC) is the latest High Efficiency Video Coding (HEVC) standard development to improve the compression performance of multi-view video plus depth (MVD) format. It adopts several additional coding tools for better representation of the dependent texture and depth data. These tools achieve high compression efficiency, but require high computational complexity. In this paper, we introduce a fast 3D-HEVC method for the complexity reduction of MVD. A simulation analysis is performed to study the coding information correlations of interview , spatio-temporal and texture-depth. The proposed fast algorithm decides to skip some treeblocks of texture and depth map at the early stage without a normal coding process, which includes fast depth level, early SKIP/Merge mode, adaptive early termination, based on the correlations of interview , spatio-temporal, and texture-depth. Experimental results show that the proposed fast scheme can save 70.2% encoding runtime of 3D-HEVC, with negligible loss of rate-distortion (RD) performance. INDEX TERMS 3D-HEVC, low complexity, texture video, depth map.

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Section: Results Of Overall Methodsmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fast and Efficient 3D-HEVC Method for Complexity Reduction Based on the Correlations of Inter-View, Spatio-Temporal, and Texture-Depth

et al. 2020

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“…Motion search plays an important role in defining the computational complexity of 3D-HEVC encoder, which is defined as the search for the best matched treeblock in temporally preceding frames. A suitable motion search window can reduce the computational complexity of 3D-HEVC and also maintain the good RD performance in 3D-HEVC [37,38]. In the joint model of 3D-HEVC, motion search has a large fixed range for a whole texture video coding.…”

Section: Adaptive Motion Search Range Adjustmentmentioning

confidence: 99%

Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

Zhang

Shuaichao

2019

Scientific Programming

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Three-dimensional extension of the high efficiency video coding (3D-HEVC) is an emerging international video compression standard for multiview video system applications. Similar to HEVC, a computationally expensive mode decision is performed using all depth levels and prediction modes to select the least rate-distortion (RD) cost for each coding unit (CU). In addition, new tools and intercomponent prediction techniques have been introduced to 3D-HEVC for improving the compression efficiency of the multiview texture videos. These techniques, despite achieving the highest texture video coding efficiency, involve extremely high-complex procedures, thus limiting 3D-HEVC encoders in practical applications. In this paper, a fast texture video coding method based on motion homogeneity is proposed to reduce 3D-HEVC computational complexity. Because the multiview texture videos instantly represent the same scene at the same time (considering that the optimal CU depth level and prediction modes are highly multiview content dependent), it is not efficient to use all depth levels and prediction modes in 3D-HEVC. The motion homogeneity model of a CU is first studied according to the motion vectors and prediction modes from the corresponding CUs. Based on this model, we present three efficient texture video coding approaches, such as the fast depth level range determination, early SKIP/Merge mode decision, and adaptive motion search range adjustment. Experimental results demonstrate that the proposed overall method can save 56.6% encoding time with only trivial coding efficiency degradation.

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3D Convolutional Neural Network for Human Behavior Analysis in Intelligent Sensor Network

Bao

Zhi

et al. 2022

Mobile Netw Appl

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Efficient multiview video plus depth coding for 3D-HEVC based on complexity classification of the treeblock

Cited by 13 publications

References 26 publications

A Fast and Efficient 3D-HEVC Method for Complexity Reduction Based on the Correlations of Inter-View, Spatio-Temporal, and Texture-Depth

A Fast and Efficient 3D-HEVC Method for Complexity Reduction Based on the Correlations of Inter-View, Spatio-Temporal, and Texture-Depth

Low-Complexity Texture Video Coding Based on Motion Homogeneity for 3D-HEVC

3D Convolutional Neural Network for Human Behavior Analysis in Intelligent Sensor Network

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