Simplified depth intra coding for 3D-HEVC based on gray-level co-occurrence matrix

Guo, Lilin; Tian, Xiang; Chen, Yaowu

doi:10.1109/siprocess.2016.7888278

Cited by 6 publications

(8 citation statements)

References 9 publications

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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%

“…Lei et al [19] proposed a simplified search algorithm for DMM1. 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.…”

Section: Typementioning

confidence: 99%

“…Angular mode is used to predict some complex texture regions. Guo et al [20] utilised the ASM of the GLCM to identify smooth CUs; DMMs are skipped for these smooth CUs. The directional features of the CU were measured by the COR of the GLCM to prune angular modes.…”

Section: Fast Candidate Mode Decisionmentioning

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.…”

Section: Introductionmentioning

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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Section: Typementioning

confidence: 99%

Section: Typementioning

confidence: 99%

Section: Fast Candidate Mode Decisionmentioning

confidence: 99%

Section: Introductionmentioning

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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“…The evaluation results of intra conventional modes and golden ratio search is proposed for fast DMM decision [10]. A feature corner point is raised to evaluatethe orientation of edges for decision of DMM skip [11]. Afast depth intra coding scheme using gray-level co-occurrence matrix to classify the coding units (CUs) into different types, and avoid checking some unnecessary modes for certain CUs [12].…”

Section: Compensated Prediction (Dcp) and Inter View Motion Predictiomentioning

confidence: 99%

Fast Mode Decision Based on Gradient Information in 3D-HEVC

et al. 2019

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The 3D extension of High Efficiency Video Coding (3D-HEVC) introduce Depth Modeling Mode (DMM) and 35 conventional intra modes to enhance the quality of coding, while bringing unacceptable computational complexity during the process of rough mode decision (RMD) and most probable mode (MPM). In this paper, we proposed a fast mode decision algorithm for texture map and depth map coding based on gradient information. Firstly, analyzing the characteristics of predict units (PU) in different intra mode applications that obtain the lowest RD-cost,then extracting the gradient information of PU to classify the PU into three types of gradient blocks and selecting appropriate candidate modes for the PU,thereby avoiding search each mode in coding process and skip the process of RMD and MPM early. Experimental results show that the proposed algorithm can achieve average 30.6% time saving with negligible reduction of coding performance.

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