Efficient Partition Decision Based on Visual Perception and Machine Learning for H.266/Versatile Video Coding

Chen, Mei-Juan; Lee, Cheng-An; Tsai, Yu-Hsiang; Yang, Chieh-Ming; Yeh, Chia‐Hung; Kau, Lih‐Jen; Chang, Chuan‐Yu

doi:10.1109/access.2022.3168155

Cited by 17 publications

(16 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ML-based solutions are proposed for HTTP adaptive streaming [14], and a reinforcement learning framework is introduced for frame-level bit allocation in HEVC/H.265 [15]. A deep convolutional neural network (DCNN) is employed for enhancing video quality in versatile video coding (VVC) [16], and human vision models and ML are leveraged for H.266/VVC encoding [17]. Deep learning is used for video streaming over the next-generation network,…”

Section: ░ 2 Related Researchmentioning

confidence: 99%

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Charles,

Khan,

Rao

2023

IJEER

View full text Add to dashboard Cite

Predicting future frames and improving inter-frame prediction are ongoing challenges in the field of video streaming. By creating a novel framework called STreamNet (Spatial-Temporal Video Coding), fusing bidirectional long short-term memory with temporal convolutional networks, this work aims to address the issue at hand. The development of STreamNet, which combines spatial hierarchies with local and global temporal dependencies in a seamless manner, along with sophisticated preprocessing, attention mechanisms, residual learning, and effective compression techniques, is the main contribution. Significantly, STreamNet claims to provide improved video coding quality and efficiency, making it suitable for next-generation networks. STreamNet has the potential to provide reliable and optimal streaming in high-demand network environments, as shown by preliminary tests that show a performance advantage over existing methods.

show abstract

Section: ░ 2 Related Researchmentioning

confidence: 99%

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Charles,

Khan,

Rao

2023

IJEER

View full text Add to dashboard Cite

show abstract

“…For the VVC standard, the work presented in Ref. 7 introduced a fast intra multitype tree (MTT) algorithm based on machine learning. The advanced method applied the random forest models to predict the intra MTT partition and skip the unnecessary ones using visual perception.…”

Section: Related Workmentioning

confidence: 99%

Fast mode decision for CU coding based on CNN for the VVC standard

et al. 2022

View full text Add to dashboard Cite

Versatile Video Coding (VVC) is the latest generation of the video coding standard.In VVC, the advanced quadtree with a nested multitype tree (QTMT) partition structure provides more flexible coding unit (CU) partition sizes compared with the quadtree (QT) decision tree structure applied in the previous High Efficiency Video Coding (HEVC) standard. This flexibility, achieved by the new QTMT partitioning improvement, considerably improves the coding performance while increasing the coding computational complexity caused mainly by the rate distortion optimization processing. To overcome the complexity issue, a fast deep intra QTMT decision tree approach based on a convolution neural network (CNN) is adopted to determine the QTMT depth decision of each 128 × 128 Coding Tree Unit (CTU). The proposed algorithm predicts both the BT depths at 32 × 32 CUs and the QT depths at 64 × 64 using trained CNNs designed for each structure instead of processing the RDcost. Experimental results prove that the suggested deep QTMT approach achieves an important complexity reduction of up to 55.51% compared with the original reference software VTM3.0, with an average of about 35% encoding time reduction accompanied by an insignificant loss in encoding performance.

show abstract

“…VVC adopts various new coding technologies [ 12 ]. Multiple coding unit partitions and numerous coding tools improve compression performance, but also greatly increase coding complexity [ 13 ]. There are already studies comparing the performance of different codecs.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Bandwidth Management Algorithm for Next-Generation Vehicular Networks

Huang,

Hu,

Cheng

2023

Sensors

View full text Add to dashboard Cite

The popularity of video services such as video call or video on-demand has made it impossible for people to live without them in their daily lives. It can be anticipated that the explosive growth of vehicular communication owing to the widespread use of in-vehicle video infotainment applications in the future will result in increasing fragmentation and congestion of the wireless transmission spectrum. Accordingly, effective bandwidth management algorithms are demanded to achieve efficient communication and stable scalability in next-generation vehicular networks. To the best of our current knowledge, a noticeable gap remains in the existing literature regarding the application of the latest advancements in network communication technologies. Specifically, this gap is evident in the lack of exploration regarding how cutting-edge technologies can be effectively employed to optimize bandwidth allocation, especially in the realm of video service applications within the forthcoming vehicular networks. In light of this void, this paper presents a seamless integration of cutting-edge 6G communication technologies, such as terahertz (THz) and visible light communication (VLC), with the existing 5G millimeter-wave and sub-6 GHz base stations. This integration facilitates the creation of a network environment characterized by high transmission rates and extensive coverage. Our primary aim is to ensure the uninterrupted playback of real-time video applications for vehicle users. These video applications encompass video conferencing, live video, and on-demand video services. The outcomes of our simulations convincingly indicate that the proposed strategy adeptly addresses the challenge of bandwidth competition among vehicle users. Moreover, it notably boosts the efficient utilization of bandwidth from less crowded base stations, optimizes the fulfillment of bandwidth prerequisites for various video applications, and elevates the overall video quality experienced by users. Consequently, our findings serve as a successful validation of the practicality and effectiveness of the proposed methodology.

show abstract

Efficient Partition Decision Based on Visual Perception and Machine Learning for H.266/Versatile Video Coding

Cited by 17 publications

References 31 publications

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Adaptive Video Coding Framework with Spatial-Temporal Fusion for Optimized Streaming in Next-Generation Networks

Fast mode decision for CU coding based on CNN for the VVC standard

An Adaptive Bandwidth Management Algorithm for Next-Generation Vehicular Networks

Contact Info

Product

Resources

About