An efficient depth modeling mode decision algorithm for 3D-HEVC depth map coding

Zhao, Jinchao; Zhao, Xiaoxin; Zhang, Weiwei; Fen-qin, Wang; Zhang, Qiuwen

doi:10.1016/j.ijleo.2016.10.008

Cited by 16 publications

(4 citation statements)

References 13 publications

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“…Online-learning-based complexity reduction scheme for 3D-HEVC [ 32 ] decreases the complexity of the inter/intra mode search process of the to-be-encoded blocks in the dependent texture views, utilizing the two-mode prediction approaches, motion information of the base texture view, and the rate distortion cost of the already encoded blocks. An efficient depth modeling mode decision algorithm for 3D-HEVC depth map coding is proposed in [ 33 ], which incorporates unnecessary depth blocks skipping approach and Contour pattern early termination method. To further relieve the computation complexity of 3D-HEVC encoders, this paper proposes a context-adaptive based CU processing algorithm for 3D-HEVC encoders with CU classification to jointly optimize CU depth level decision, mode decision, ME and DE processes.…”

Section: Overview Of Related Work On Fast Encoding Methodsmentioning

confidence: 99%

Context-adaptive based CU processing for 3D-HEVC

Shen

2017

PLoS ONE

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The 3D High Efficiency Video Coding (3D-HEVC) standard aims to code 3D videos that usually contain multi-view texture videos and its corresponding depth information. It inherits the same quadtree prediction structure of HEVC to code both texture videos and depth maps. Each coding unit (CU) allows recursively splitting into four equal sub-CUs. At each CU depth level, it enables 10 types of inter modes and 35 types of intra modes in inter frames. Furthermore, the inter-view prediction tools are applied to each view in the test model of 3D-HEVC (HTM), which uses variable size disparity-compensated prediction to exploit inter-view correlation within neighbor views. It also exploits redundancies between a texture video and its associated depth using inter-component coding tools. These achieve the highest coding efficiency to code 3D videos but require a very high computational complexity. In this paper, we propose a context-adaptive based fast CU processing algorithm to jointly optimize the most complex components of HTM including CU depth level decision, mode decision, motion estimation (ME) and disparity estimation (DE) processes. It is based on the hypothesis that the optimal CU depth level, prediction mode and motion vector of a CU are correlated with those from spatiotemporal, inter-view and inter-component neighboring CUs. We analyze the video content based on coding information from neighboring CUs and early predict each CU into one of five categories i.e., DE-omitted CU, ME-DE-omitted CU, SPLIT CU, Non-SPLIT CU and normal CU, and then each type of CU adaptively adopts different processing strategies. Experimental results show that the proposed algorithm saves 70% encoder runtime on average with only a 0.1% BD-rate increase on coded views and 0.8% BD-rate increase on synthesized views. Our algorithm outperforms the state-of-the-art algorithms in terms of coding time saving or with better RD performance.

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Section: Overview Of Related Work On Fast Encoding Methodsmentioning

confidence: 99%

Context-adaptive based CU processing for 3D-HEVC

Shen

2017

PLoS ONE

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“…Fast depth intra coding algorithms I C h e n [ 12], Fu [13], Saldanha [14], Chen [15] II Shen [16], Hamout [17], Zhang [18], Lei [19], Guo [20] Zhao [21], Sanchez [22]- [23], Park [24], Zhang [25] III Li [26], Peng [27], Jing [28] is listed in Table 1. The type I is to reduce the number of unnecessary traversal coding units (CUs) or terminate CU partitioning early, the type II is to reduce the prediction modes or optimise DMMs, and the type III is a comprehensive algorithm including the first two types.…”

Section: Typementioning

confidence: 99%

“…Guo et al [20] proposed a fast depth intra coding scheme using gray-level co-occurrence matrix (GLCM) to classify the CUs into different types, and avoided checking some unnecessary modes for certain CUs. Zhao et al [21] optimised DMMs based on the depth of the scene represented by the depth value. Sanchez et al [22] proposed a fast intra modes scheme by reducing candidate modes and optimise DMM1 [23].…”

Section: Typementioning

confidence: 99%

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Fast depth intra coding based on texture feature and spatio‐temporal correlation in 3D‐HEVC

Wang

et al. 2020

IET Image Processing

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To alleviate the computation burden of the depth intra coding in 3D‐HEVC, a complexity reduction scheme based on texture feature and spatio‐temporal correlation is proposed. Firstly, a maximum splitting depth layer decision algorithm is proposed to reduce unnecessary splitting depth layer of the coding tree unit utilising the information of the previous encoded I frame in the same view. Secondly, a new texture complexity model is built by pixel‐based statistical method combined with edge detection. Based on the proposed model, the coding unit block is divided into the smooth block, texture or edge block. On the coding unit level, an early termination of coding unit splitting algorithm for smooth blocks is proposed to filter out unnecessary coding blocks. Thirdly, on the predicting unit level, a fast candidate mode decision algorithm considering predicting unit's types and spatial correlation is proposed to decide the candidate mode list directly. Experimental results describe that the proposed algorithm reduces 53.8% depth intra coding time on average, with 0.43% BD‐rate loss on synthesised views.

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Multiple Linear Regression Analysis of Factors Affecting the Consumption

Silva

Lezama

Solano

2019

Inventive Computation Technologies

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Econometrics provides the researchers with methods, theoretical basements, and procedures that allow the formulation and estimation of economic models that explain the study variable during a reference time period, as well as making predictions about the behavior of the studied reality based on the explanatory variables. The entire process, analyzed from econometrics after having formulated and estimated the model, leads to a very important phase: the statistical validation, which helps the researcher to ensure that the model satisfactorily passes a series of tests. These tests will allow the use of the model not just to explain the behavior of the independent variable under study, but to make predictions based on scenarios of occurrence based on those explanatory variables included in the model, offering a theoretical-practical support to formulate policies related to the studied phenomenon. This research aims to generate the first elements to know the private consumption behavior in India in the period from 2012 to 2018

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An efficient depth modeling mode decision algorithm for 3D-HEVC depth map coding

Cited by 16 publications

References 13 publications

Context-adaptive based CU processing for 3D-HEVC

Context-adaptive based CU processing for 3D-HEVC

Fast depth intra coding based on texture feature and spatio‐temporal correlation in 3D‐HEVC

Multiple Linear Regression Analysis of Factors Affecting the Consumption

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