Early termination for fast intra mode decision in depth map coding using DIS-inheritance

Fu, Chang-Hong; Chen, Hao; Chan, Yui-Lam; Tsang, Sik-Ho; Zhu, Xiaohua

doi:10.1016/j.image.2019.115644

Cited by 10 publications

(16 citation statements)

References 18 publications

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“…Fu's [13] integrates the Restrict‐PRO method into Zhang's [25], further reducing the complexity of CU partitioning. Fu's [13] achieved 50.1% encoding time saving with 0.64% BD‐rate loss. Compared with the [13, 22 25], the proposed overall algorithm has reduced large the encoding time, while maintaining the same visual quality as the encoder.…”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

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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“…For MV-HEVC and 3D-HEVC, there is one extra co-located CTU in the interview frame, and for 3D-HEVC, there is also one extra CTU in the depth frame [15]. Numerous approaches are proposed that specifically aim to exploit those extra neighboring CTUs in these HEVC extensions [40,41,42,43,44,45,47,48,49,50,19,51,52]. These methods also follow similar approaches as methods proposed for standard HEVC.…”

Section: Discussionmentioning

confidence: 99%

“…Fu et al [49] utilize Depth Intra Skip (DIS) for depth map coding, which directly uses reconstructed value of spatial neighboring CUs to represent the current CU for 3D-HEVC.…”

Section: Mv-hevc and 3d-hevcmentioning

confidence: 99%

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

Amirpour

Ghanbari

et al. 2021

Signal Processing: Image Communication

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High-Efficiency Video Coding (HEVC) surpasses its predecessors in encoding efficiency by introducing new coding tools at the cost of an increased encoding time-complexity. The Coding Tree Unit (CTU) is the main building block used in HEVC. In the HEVC standard, frames are divided into CTUs with the predetermined size of up to 64 × 64 pixels. Each CTU is then divided recursively into a number of equally sized square areas, known as Coding Units (CUs). Although this diversity of frame partitioning increases encoding efficiency, it also causes an increase in the time complexity due to the increased number of ways to find the optimal partitioning. To address this complexity, numerous algorithms have been proposed to eliminate unnecessary searches during partitioning CTUs by exploiting the correlation in the video. In this paper, existing CTU depth decision algorithms for HEVC are surveyed. These algorithms are categorized into two groups, namely statistics and machine learning approaches. Statistics approaches are further subdivided into neighboring and inherent approaches. Neighboring approaches exploit the similarity between adjacent CTUs to limit the depth range of the current CTU, while inherent approaches use only the available information within the current CTU. Machine learning approaches try to extract and exploit similarities implicitly. Traditional methods like support vector machines or random forests use manually selected features, while recently proposed deep learning methods extract features during training. Finally, this paper discusses extending these methods to more recent video coding formats such as Versatile Video Coding (VVC) and AOMedia Video 1 (AV1).

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“…They exploited inter-layer correlations to predict candidate depths, then used correlations to predict probable intra-modes, and finally adopted residual coefficients to early terminate inter-layer reference modes and depths. In the 3D extension of HEVC, Fu et al [14] proposed an early termination scheme for fast intra-mode decision in depth maps. Moreover, focusing on 3D-HEVC, Li et al [15] proposed a self-learning residual model -based fast CU size decision approach for the intra-coding of both texture views and depth maps.…”

Section: Related Workmentioning

confidence: 99%

“…The second category consists of the algorithms based on advanced machine learning techniques, such as Support Vector Machine (SVM) [9], Decision Trees [10], Bayesian method with conditional random fields [11] and Neural Networks [12,13]. Though some statistical information based fast algorithms can achieve a good performance [14,15], the statistical distributions and thresholds are different from sequence to sequence. Moreover, their performances are highly dependent on special video sequence.…”

Section: Introductionmentioning

confidence: 99%

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

Peng

et al. 2020

Electronics

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High Efficiency Video Coding (HEVC) has achieved about 50% bit-rates saving compared with its predecessor H.264 standard, while the encoding complexity increases dramatically. Due to the introduction of more flexible partition structures and more optional prediction directions, HEVC takes a brute force approach to find the optimal partitioning result which is much more time consuming. Therefore, this paper proposes a bagged trees based fast approach (BTFA) and focuses on the coding unit (CU) size decision for HEVC intra-coding. First, several key features of a target CU are extracted for three-output classifiers. Then, to avoid feature extraction and prediction time over head, our approach is designed frame-wisely, and the procedure is applied parallel with the encoding process. Using the adaptive threshold determination algorithm, our approach achieves 42.04% time saving with negligible 0.92% Bit-Distortion (BD)-rate loss. Furthermore, in order to calculate the optimal thresholds to balance BD-rate loss and complexity reduction, the neural network based mathematical fitting is added to BTFA, which is called the advanced bagged trees based fast approach (ABTFA). Finally, experimental results show that ABTFA achieves 47.87% time saving with only 0.96% BD-rate loss, which outperforms other state-of-the-art approaches.

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Early termination for fast intra mode decision in depth map coding using DIS-inheritance

Cited by 10 publications

References 18 publications

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

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

CTU depth decision algorithms for HEVC: A survey

Bagged Tree Based Frame-Wise Beforehand Prediction Approach for HEVC Intra-Coding Unit Partitioning

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