Fast CU size decision and intra-prediction mode decision method for H.266/VVC

Li, Mingying; Wang, Zhiying; Zhang, Qiuwen

doi:10.1186/s13640-024-00622-7

Cited by 1 publication

(2 citation statements)

References 31 publications

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“…Wang et al proposed a fast CU decision algorithm based on FSVMs and DAG-SVMs for coding complexity reduction, which divides the CU-partitioning process into two stages and symmetrically extracts some of the same CU features [21]. 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].…”

Section: Machine Learning Methodsmentioning

confidence: 99%

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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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Section: Machine Learning Methodsmentioning

confidence: 99%

“…We compare our algorithm with three of the most advanced methods, namely, those of Ni 2022 [14], Wang 2023 [21], and Li 2024 [22]. As shown in Table 8, the T/B = TS/BDBR denotes the measurement for the trade-off between the time savings and BDBR performance.…”

Section: Comparison With Othersmentioning

confidence: 99%