Light-weight CNN-based VVC Inter Partitioning Acceleration
The Versatile Video Coding (VVC) standard has been finalized by Joint Video Exploration Team (JVET) in 2020. Compared to the High Efficiency Video Coding (HEVC) standard, VVC offers about 50% compression efficiency gain, in terms of Bjontegaard Delta-Rate (BD-rate), at the cost of about 10x more encoder complexity. In this paper, we propose a Convolutional Neural Network (CNN)-based method to speed up inter partitioning in VVC. Our method operates at the Coding Tree Unit (CTU) level, by splitting each CTU into a fixed grid of 8x8 blocks. Then each cell in this grid is associated with information about the partitioning depth within that area. A lightweight network for predicting this grid is employed during the rate-distortion optimization to limit the Quaternary Tree (QT)-split search and avoid partitions that are unlikely to be selected. Experiments show that the proposed method can achieve acceleration ranging from 17% to 30% in the RandomAccess Group Of Picture 32 (RAGOP32) mode of VVC Test Model (VTM)10 with a reasonable efficiency drop ranging from 0.37% to 1.18% in terms of BD-rate increase.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
AccEPT: An Acceleration Scheme for Speeding Up Edge Pipeline-parallel Training
It is usually infeasible to fit and train an entire large deep neural network (DNN) model using a single edge device due to the limited resources. To facilitate intelligent applications across edge devices, researchers h…
Data CompressionSPEED: Streaming Partition and Parallel Acceleration for Temporal Interaction Graph Embedding
Temporal Interaction Graphs (TIGs) are widely employed to model intricate real-world systems such as financial systems and social networks. To capture the dynamism and interdependencies of nodes, existing TIG embedding m…
CPUGPUGraph EmbeddingScalable Multi-GPU Simulation of 3D Multicellular Growth with RNN-Based Workload Balancing
Detailed multicellular growth simulations based on subcellular element models (SEMs) can capture complex tissue development, but their element-level interactions impose substantial computational cost. This work presents …
Design and Prototyping Distributed CNN Inference Acceleration in Edge Computing
For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we implement a working prototype of a new …
Distributed ComputingEdge-computingModel CompressionModel Selection+1Designing a lightweight 1D convolutional neural network with Bayesian optimization for wheel flat detection using carbody accelerations:
Many freight waggons in Europe have been recently equipped with embedded systems (ESs) for vehicle tracking. This provides opportunities to implement the real-time fault diagnosis algorithm on ESs without additional inve…
Bayesian OptimizationFault Diagnosis