A VVC Intra Rate Control With Small Bit Fluctuations Using a Lagrange Multiplier Adjustment
- Authors
- Hyun, Myung Han; Lee, Bumshik; Kim, Munchurl
- Issue Date
- Jun-2024
- Publisher
- Institute of Electrical and Electronics Engineers
- Keywords
- rate-distortion optimization (RDO); lagrange multiplier adjustment (LMA); rate control (RC); versatile video coding (VVC)
- Citation
- IEEE Transactions on Multimedia, v.26, pp 6811 - 6821
- Pages
- 11
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Multimedia
- Volume
- 26
- Start Page
- 6811
- End Page
- 6821
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/70820
- DOI
- 10.1109/TMM.2024.3355633
- ISSN
- 1520-9210
1941-0077
- Abstract
- Since the emergence of high-quality multimedia processing applications such as video streaming, digital editing, archiving, etc. these days, an intra coding rate control (RC) is becoming an indispensable and important technology. In this article, a frame-level intra RC scheme for Versatile Video Coding (VVC) using a Lagrange multiplier adjustment (LMA) is proposed. The VVC test model (VTM) uses an R-lambda model-based rate control. However, the estimation performance of target bits based on an R-lambda-QP relation is decreased because the distortion dependencies among consecutive frames are not considered especially for intra RC. Thus, in a rate-distortion optimization (RDO) based encoding, the lambda values determined for given quantization parameter (QP) values should be elaborately controlled to increase the target bits estimation performance. In our work, we focus on the intra RC scheme by taking advantage of particle-filtering-based prediction (PFP) for distortion estimates, and precise per-frame lambda values can be derived for an appropriate RDO process that can lead to small bit-fluctuations. Our extensive experimental results demonstrate that our RC scheme using the per-frame LMA is superior to the default RC (VTM-16.0rc1) method and the state-of-the-art RC methods with significant margins of average 15.57%, 15.31% and 31.13% improvements in terms of the normalized root mean square error (NRMSE) for All Intra (AI) configuration of VVC, respectively.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - ETC > Journal Articles

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.