Inter-view prediction of coding tree depth for HEVC-based multiview video coding

Silva, Thaísa Leal da; Cruz, Luís A. da Silva; Agostini, Luciano

doi:10.1109/icecs.2013.6815380

Cited by 5 publications

(9 citation statements)

References 11 publications

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“…2. Four spatial neighboring CTUs (1)(2)(3)(4) are in the same frame of the current CTU, and in the reference frame there are nine temporal neighboring CTUs (5)(6)(7)(8)(9)(10)(11)(12)(13). We aim to understand the correlation of the optimal CU decisions between the current CTU and any one of the neighboring CTUs.…”

Section: Classification Of the Neighboring Ctusmentioning

confidence: 99%

“…The work in [4] proposes a CU depth pruning algorithm, which can reduce encoding time by about 10% with only 0.1% coding loss. A new technique utilizing the coding unit tree depths in the base view as a depth threshold in the dependent views is proposed in [5] for multi-view video coding. This new technique achieves a complexity reduction by up to 50.32% when compared to HEVC simulcast.…”

Section: Introductionmentioning

confidence: 99%

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Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

Liu

Lin

Chou

2016

Journal of Visual Communication and Image Representation

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Section: Classification Of the Neighboring Ctusmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

Liu

Lin

Chou

2016

Journal of Visual Communication and Image Representation

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“…To the best of our knowledge, very few researchers have worked on reducing the complexity of MV-HEVC. In [24], the researchers proposed an inter-view prediction method for the HEVC-based multi-view video. The aforesaid work mostly used the maximum depth of the co-located CTU of the base view as the threshold for corresponding CUs of dependent views.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Moreover, the prediction of the depth threshold is achieved by nine CTUs instead of using the CU neighborhood. It addresses the weakness of the method in [24]. In our proposed method, we further improve it by adding temporal levels to the threshold prediction.…”

Section: Literature Reviewmentioning

confidence: 99%

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Early CU Depth Decision and Reference Picture Selection for Low Complexity MV-HEVC

et al. 2019

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The Multi-View extension of High Efficiency Video Coding (MV-HEVC) has improved the coding efficiency of multi-view videos, but this comes at the cost of the extra coding complexity of the MV-HEVC encoder. This coding complexity can be reduced by efficiently reducing time-consuming encoding operations. In this work, we propose two methods to reduce the encoder complexity. The first one is Early Coding unit Splitting (ECS), and the second is the Efficient Reference Picture Selection (ERPS) method. In the ECS method, the decision of Coding Unit (CU) splitting for dependent views is made on the CU splitting information obtained from the base view, while the ERPS method for dependent views is based on selecting reference pictures on the basis of the temporal location of the picture being encoded. Simulation results reveal that our proposed methods approximately reduce the encoding time by 58% when compared with HTM (16.2), the reference encoder for MV-HEVC.

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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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Inter-view prediction of coding tree depth for HEVC-based multiview video coding

Cited by 5 publications

References 11 publications

Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

Efficient prediction of CU depth and PU mode for fast HEVC encoding using statistical analysis

Early CU Depth Decision and Reference Picture Selection for Low Complexity MV-HEVC

CTU depth decision algorithms for HEVC: A survey

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