Fast CU Size Decisions for HEVC Inter-Prediction Using Support Vector Machines

Erabadda, Buddhiprabha; Mallikarachchi, Thanuja; Kulupana, Gosala; Fernando, Anil

doi:10.23919/eusipco.2019.8903081

Cited by 5 publications

(2 citation statements)

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“…SVMs are usually used for classification tasks, but they can also be used for regression. SVMs are commonly used for CTU partitioning [82,83,84,85,86,87,88,89,90,91,92].…”

Section: Support Vector Machinesmentioning

confidence: 99%

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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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“…SVMs are usually used for classification tasks, but they can also be used for regression. SVMs are commonly used for CTU partitioning [82,83,84,85,86,87,88,89,90,91,92].…”

Section: Support Vector Machinesmentioning

confidence: 99%

“…A two stage algorithm for CTU partitioning is proposed by Erabadda et al [91]. In the first stage, offline trained SVMs are used to make the CU splitting decision, and the second stage is used to apply the decision of the SVMs.…”

Section: Support Vector Machinesmentioning

confidence: 99%