Encoder-Decoder-Based Intra-Frame Block Partitioning Decision

The recursive intra-frame block partitioning decision process, a crucial component of the next-generation video coding standards, exerts significant influence over the encoding time. In this paper, we propose an encoder-decoder neural network (NN) to accelerate this process. Specifically, a CNN is u...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Jiang, Yucheng, Han, Peng, Song, Yan, Yu, Jie, Zhang, Peng, Mai, Songping
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 10.10.2023
Subjects
Algorithms
Artificial neural networks
Coding
Encoders-Decoders
High definition
Parallel processing
Partitioning
Online AccessGet full text

Cover

More Information
Summary:The recursive intra-frame block partitioning decision process, a crucial component of the next-generation video coding standards, exerts significant influence over the encoding time. In this paper, we propose an encoder-decoder neural network (NN) to accelerate this process. Specifically, a CNN is utilized to compress the pixel data of the largest coding unit (LCU) into a fixed-length vector. Subsequently, a Transformer decoder is employed to transcribe the fixed-length vector into a variable-length vector, which represents the block partitioning outcomes of the encoding LCU. The vector transcription process adheres to the constraints imposed by the block partitioning algorithm. By fully parallelizing the NN prediction in the intra-mode decision, substantial time savings can be attained during the decision phase. The experimental results obtained from high-definition (HD) sequences coding demonstrate that this framework achieves a remarkable 87.84\% reduction in encoding time, with a relatively small loss (8.09\%) of coding performance compared to AVS3 HPM4.0.
ISSN:2331-8422