APSIPA Transactions on Signal and Information Processing > Vol 13 > Issue 6

Learning-Based QP Initialization for Versatile Video Coding

Zhentao Zhang, Fuzhou University, China, Hongji Zeng, Fuzhou University, China, Jielian Lin, Putian University, China AND Fuzhou University, China, ljlchenyi@gmail.com
 
Suggested Citation
Zhentao Zhang, Hongji Zeng and Jielian Lin (2024), "Learning-Based QP Initialization for Versatile Video Coding", APSIPA Transactions on Signal and Information Processing: Vol. 13: No. 6, e500. http://dx.doi.org/10.1561/116.20240029

Publication Date: 30 Oct 2024
© 2024 Z. Zhang, H. Zeng and J. Lin
 
Subjects
Deep learning,  Speech/audio/image/video compression
 
Keywords
Bit rate controlresidual networkvideo coding
 

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This is published under the terms of CC BY-NC.

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In this article:
Introduction 
Related Work 
Proposed Method 
Experiments 
Conclusions 
References 

Abstract

Versatile Video Coding (VVC) is a modern video compression standard designed to efficiently encode high definition video content, regardless of its diversity. It is expected to deliver superior compression performance compared to the previous standard, High Efficiency Video Coding (HEVC). However, the bit rate control problem for VVC can still be improved. To address this issue, a learning-based initial frame Quantization Parameter (QP) prediction algorithm has been proposed in this paper. This algorithm extracts information from image pixels and maps it to a feature matrix to reduce its additional cost. Furthermore, the problem of inaccurate determination of VVC QPs has been addressed by building a residual network to represent the frame complexity progressively and learning the optimal relationship between QPs and the target bit rate. Experimental results show that the proposed method reduces the control error from 10.74% to 7.19% compared to the original encoder.

DOI:10.1561/116.20240029

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APSIPA Transactions on Signal and Information Processing Special Issue - Deep Learning-Based Data Compression
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