Speed Up VVC Intra-Coding by Learned Models and Feature Statistics

Chen, Jiann-Jone; Chou, Yeh-Guan; Jiang, Chi-Shiun

doi:10.1109/access.2023.3329717

Cited by 2 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wu et al proposed a hierarchical grid fully convolutional network to predict the QTMT partition structure for fast VVC intra coding [27]. Chen et al proposed to predict CU partition modes for VVC intra coding by a CNN model, which is trained with the neighboring line pixels and quantization parameters [28]. In [29], heuristic and deep learning methods were combined.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

“…A quantitative analysis comparing our model with deep learning-based methods is presented in Table 9. This includes methods from Zan 2023 [32], Wu 2022 [27], and Chen 2023 [28]. It can be seen that our algorithm on top of VTM 23.1 achieves the maximum time saving of 69.85% with the minimum BDBR loss of 1.65% (the time saving of the proposed method on top VTM 7.0 is slightly reduced to 66.69%).…”

Section: Comparison With Othersmentioning

confidence: 95%

See 1 more Smart Citation

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

Chen,

Cheng,

Zhu

et al. 2024

Electronics

View full text Add to dashboard Cite

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.

show abstract

Section: Deep Learning Methodsmentioning

confidence: 99%

Section: Comparison With Othersmentioning

confidence: 95%

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

Chen,

Cheng,

Zhu

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

Supporting Differentiated Streaming Services in Heterogeneous Vehicle-to-Everything Networks

Huang,

Hu,

Cheng

et al. 2024

Sensors

View full text Add to dashboard Cite

Advancements in assisted driving technologies are expected to enable future passengers to use a wide range of multimedia applications in electric vehicles (EVs). To address the bandwidth demands for high-resolution and immersive videos during peak traffic, this study introduces a bandwidth-management algorithm to support differentiated streaming services in heterogeneous vehicle-to-everything (V2X) networks. By leveraging cellular 6G base stations, along with Cell-Free (CF) Massive Multi-Input Multi-Output (mMIMO) Wi-Fi 7 access points, the algorithm aims to provide a high-coverage, high-speed, and low-interference V2X network environment. Additionally, Li-Fi technology is employed to supply extra bandwidth to vehicles with limited connectivity via V2V communication. Importantly, the study addresses the urgency and prioritization of different applications to ensure the smooth execution of emergency applications and introduces a pre-downloading mechanism specifically for non-real-time applications. Through simulations, the algorithm’s effectiveness in meeting EV users’ bandwidth needs for various multimedia streaming applications is demonstrated. During peak-bandwidth-demand periods, users experienced an average increase in bandwidth of 47%. Furthermore, bandwidth utilization across the V2X landscape is significantly improved.

show abstract

Speed Up VVC Intra-Coding by Learned Models and Feature Statistics

Cited by 2 publications

References 21 publications

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

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

Supporting Differentiated Streaming Services in Heterogeneous Vehicle-to-Everything Networks

Contact Info

Product

Resources

About