Fast Depth Map Intra Coding for 3D Video Compression Based Tensor Feature Extraction and Data Analysis

Hamout, Hamza; Elyousfi, Abderrahmane

doi:10.1109/tcsvt.2019.2918770

Cited by 19 publications

(8 citation statements)

References 32 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%

“…Test conditions are used as follows: three-view case: center view -left view -right view (in coding order), QP are set as (25,34), (30,39), (35,42) and (40,45), the maximum coding unit is 64×64 pixels, a max partition depth is 4. In this section, we compare the proposed fast method with HTM16.1 and the state-of-the-art fast methods [33], [35], [36], [38], where the compression efficiency is measured by BDBR [42], and " TS " means the entire encoding time saving compared with 3D-HEVC, which is defined as follow, (10) where proposed Time and origional Time denote encoding time of the proposed scheme and original 3D-HEVC for same test video, respectively. In Tables VIII-X, " t TS ", " d TS ", and " o TS " represent run time savings of the texture, depth, and overall coding, respectively.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…It reports averaged 58.3% complexity reduction with 0.13% bit-rate loss for 3D-HEVC. A fast depth map intra mode decision method is developed in [33] to assign computational complexity based on tensor feature extraction and data analysis, which can selectively omit unnecessary depth modes in the 3D-HEVC mode decision process.…”

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%

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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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“…With respect to the setting for voice data extraction, a sample extraction rate of 16 kHz was set, and the size of the window was set as 32 ms by referring to existing studies. To minimize data loss, the window overlapping was established as 66% [38,39]. e HR data of six normal subjects were collected for one week during which they performed daily activities.…”

Section: Experiments Environmentmentioning

confidence: 99%

Modeling of Child Stress-State Identification Based on Biometric Information in Mobile Environment

Kim

Mesicek

Kim

2021

Mobile Information Systems

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A technology must be developed to automatically identify extreme stress states of children who cannot properly express their emotions when recognizing dangerous situations, which threaten the safety of children, in real time. This study presents a stress-state identification model for children based on machine learning, biometric data, a smart band for collecting biometric data, and a mobile application for monitoring the stress state of the child classified. In addition, through an experiment comparing a dataset using only voice data and a dataset using both voice and heart rate data, we aimed to verify the effectiveness of the combination of the two biosignal datasets. As a result of the experiment, the SVM model showed the highest performance with an accuracy of 88.53% for the dataset using both voice data and heart rate data. The results of this study presented strong implications for the possibility of automating the stress-state identification of a child, and it is expected that the developed method can be used to take preventive measures for dangerous situations to children.

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“…Based on edge detection, Wang et al [12] proposed a fast intra modes decision algorithm for depth map by determining whether the Depth Modeling Modes (DMMs) need to be traversed. Hamout et al [13] utilized fast depth map intra algorithms based on tensor feature extraction and data analysis to skip unnecessary conventional intra prediction modes and DMMs.…”

Section: Related Workmentioning

confidence: 99%

Fast Depth Video Coding with Intra Prediction on VVC

2020

KSII TIIS

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In the stereoscopic or multiview display, the depth video illustrates visual distances between objects and camera. To promote the computational efficiency of depth video encoder, we exploit the intra prediction of depth videos under Versatile Video Coding (VVC) and observe a diverse distribution of intra prediction modes with different coding unit sizes. We propose a hybrid scheme to further boost fast depth video coding. In the first stage, we adaptively predict the HADamard (HAD) costs of intra prediction modes and initialize a candidate list according to the HAD costs. Then, the candidate list is further improved by considering the probability distribution of candidate modes with different CU sizes. Finally, early termination of CU splitting is performed at each CU depth level based on the Bayesian theorem. Our proposed method is incorporated into VVC intra prediction for fast coding of depth videos. Experiments with 7 standard sequences and 4 Quantization parameters (Qps) validate the efficiency of our method.

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Fast Depth Map Intra Coding for 3D Video Compression Based Tensor Feature Extraction and Data Analysis

Cited by 19 publications

References 32 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

Modeling of Child Stress-State Identification Based on Biometric Information in Mobile Environment

Fast Depth Video Coding with Intra Prediction on VVC

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