Adaptive keypoint-based CU depth decision for HEVC intra coding

Kim, Nam-Uk; Jeon, Seungsu; Shim, Hiuk Jae; Lim, Sung‐Chang; Ko, Hyunsuk

doi:10.1109/bmsb.2016.7521923

Cited by 28 publications

(5 citation statements)

References 5 publications

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“…In addition to the above two categories of traditional algorithms with more research, there are also many methods. [12] utilized the extraction of the number of points with higher frequencies and determined the final CU mode based on this segmentation, and [13] utilized fast CU partitioning decisions with SAO edge category information as spatial-temporal encoding parameters.…”

Section: A Hevc Methodsmentioning

confidence: 99%

Fast CU Partitioning Algorithm for VVC Based on CNN and FSVM

Song,

LI,

Zhang

2024

IEEE Access

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The new coding standard Versatile Video Coding (VVC) introduces additional encoding techniques based on the existing video coding standard, such as the difference in block partition structures. While these new technologies bring about enhanced encoding performance, they also result in a significant increase in encoding time complexity. In the paper, we propose a decision algorithm to partition fast CUs, which is based on CNN networks and FSVM. Initially, the algorithm utilizes depth information formed by combining inter-frame correlations as input to our trained CNN model, predicting the optimal depth for encoding.Following that, the second algorithm based on FSVM is introduced. The F-Score method is employed to extract appropriate features for constructing FSVM. Within the predicted depth range from the initial algorithm, the CU partitioning mode is predicted, leading to an additional reduction in encoding complexity. Our experimental results demonstrate that the proposed algorithm can save 53.55% of encoding time, with a marginal increase of only 1.47% in BDBR.It achieves a favorable balance between video quality and encoding speed.

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Section: A Hevc Methodsmentioning

confidence: 99%

Fast CU Partitioning Algorithm for VVC Based on CNN and FSVM

Song,

LI,

Zhang

2024

IEEE Access

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“…Among these techniques, Xiong et al proposed a fast CU partition method based on pyramid motion divergence by studying the implicit relationship between motion divergence and the rate distortion (RD) cost [6]. Kim et al proposed an adaptive CU depth decision-making scheme based on key points [7]. Shen et al proposed making CU size decisions by using important and computationally friendly features, and a Bayesian decision rule was defined to help accurately and quickly select the CU size by minimizing the Bayesian risk at the same time [8].…”

Section: The Related Workmentioning

confidence: 99%

“…It is the key factor leading to the increase of intra coding complexity. To solve this problem, some traditional exploratory methods have emerged in the past few years [6][7][8][9][10]. These methods summarize the laws and intermediate characteristics through mathematical statistics, so as to skip the unnecessary recursive search process.…”

Section: Introductionmentioning

confidence: 99%

A Complexity-Reducing HEVC Intra-Mode Method Based on VGGNet

Qin¹,

Qin²,

Zhu³

et al. 2022

Journal of Compurters

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<p>High-efficiency video coding (HEVC) has improved the coding performance by 50% compared with the previous H.264 coding standard. However, it has also introduced an extremely high coding complexity. The quad-tree partition used by the coding unit (CU) is one of the key factors leading to the increase in complexity. Therefore, this paper proposes a CU partition method based on a convolutional neural net-work (CNN). Aiming at the complex recursive calculation of CU partition, an improved VGGNet network structure is proposed to replace the brute-force search strategy, which effectively reduces the computa-tional complexity of intra frame coding. Finally, to enhance the effectiveness of the network model in this paper, the feature pyramid network is added to the CNN model to improve the accuracy of feature extraction. The experimental results show that the proposed method can reduce the intra coding time by 59.71% while maintaining the coding performance.</p> <p> </p>

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“…A key-point detector that finds high-frequency areas in the image is used to decide on CU depth search range by Kim et al [64]. The primary motivation behind this approach is that, in HEVC, high-frequency areas are given higher CU depths.…”

Section: Texture Complexitymentioning

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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Adaptive keypoint-based CU depth decision for HEVC intra coding

Cited by 28 publications

References 5 publications

Fast CU Partitioning Algorithm for VVC Based on CNN and FSVM

Fast CU Partitioning Algorithm for VVC Based on CNN and FSVM

A Complexity-Reducing HEVC Intra-Mode Method Based on VGGNet

CTU depth decision algorithms for HEVC: A survey

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