Content Adaptive Fast CU Size Selection for HEVC Intra-Prediction

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

doi:10.1109/icce.2019.8662119

Cited by 3 publications

(5 citation statements)

References 6 publications

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“…SVMs and RFs are still the main traditional approaches due to their lower complexity and simplicity. There still exists recent works that benefit from them [88,89,90,92,96,98,100]. Recently, multiple SVMs and RFs have been proposed more commonly [85,86,87,88,89,90,92,96,98].…”

Section: Discussionmentioning

confidence: 99%

“…There still exists recent works that benefit from them [88,89,90,92,96,98,100]. Recently, multiple SVMs and RFs have been proposed more commonly [85,86,87,88,89,90,92,96,98]. This allows each SVM or RF to focus on a specific feature which results in a better prediction and generalization.…”

Section: Discussionmentioning

confidence: 99%

“…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%

“…Erabadda et al [90] use weighted SVMs that are trained online during encoding cycle in a two layered structure to determine CU depth early termination. In the the first level, two SVMs are used for each CU depth, and training data is collected using default RD optimization of HEVC.…”

Section: Support Vector Machinesmentioning

confidence: 99%

See 3 more Smart Citations

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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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…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%

Section: Support Vector Machinesmentioning

confidence: 99%

See 2 more Smart Citations

CTU depth decision algorithms for HEVC: A survey

Çetinkaya

Amirpour

Ghanbari

et al. 2021

Signal Processing: Image Communication

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show abstract

“…The fast CU size decision method was guided by the SVM classification of the complexity degree [22]. Erabadda et al devised a weighted SVM-based CU size selection algorithm [23]. This algorithm extracts texture complexity, RD cost and context information to train the model.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

Fast Versatile Video Coding (VVC) Intra Coding for Power-Constrained Applications

Chen,

Cheng,

Zhu

et al. 2024

Electronics

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Versatile Video Coding (VVC) achieves impressive coding gain improvement (about 40%+) over the preceding High-Efficiency Video Coding (HEVC) technology at the cost of extremely high computational complexity. Such an extremely high complexity increase is a great challenge for power-constrained applications, such as Internet of video things. In the case of intra coding, VVC utilizes the brute-force recursive search for both the partition structure of the coding unit (CU), which is based on the quadtree with nested multi-type tree (QTMT), and 67 intra prediction modes, compared to 35 in HEVC. As a result, we offer optimization strategies for CU partition decision and intra coding modes to lessen the computational overhead. Regarding the high complexity of the CU partition process, first, CUs are categorized as simple, fuzzy, and complex based on their texture characteristics. Then, we train two random forest classifiers to speed up the RDO-based brute-force recursive search process. One of the classifiers directly predicts the optimal partition modes for simple and complex CUs, while another classifier determines the early termination of the partition process for fuzzy CUs. Meanwhile, to reduce the complexity of intra mode prediction, a fast hierarchical intra mode search method is designed based on the texture features of CUs, including texture complexity, texture direction, and texture context information. Extensive experimental findings demonstrate that the proposed approach reduces complexity by up to 77% compared to the latest VVC reference software (VTM-23.1). Additionally, an average coding time saving of 70% is achieved with only a 1.65% increase in BDBR. Furthermore, when compared to state-of-the-art methods, the proposed method also achieves the largest time saving with comparable BDBR loss. These findings indicate that our method is superior to other up-to-date methods in terms of lowering VVC intra coding complexity, which provides an elective solution for power-constrained applications.

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