Jia P, Koyuncu AB, Mao J, Cui Z, Ma Y, Guo T, Solovyev T, Karabutov A, Zhao Y, Wang J, Alshina E, Kaup A (2024)
Publication Type: Conference contribution
Publication year: 2024
Publisher: Institute of Electrical and Electronics Engineers Inc.
Conference Proceedings Title: 2024 Picture Coding Symposium, PCS 2024 - Proceedings
Event location: Taichung, TWN
ISBN: 9798350358483
DOI: 10.1109/PCS60826.2024.10566454
The research on neural network (NN) based image compression has shown superior performance compared to classical compression frameworks. Unlike the hand-engineered transforms in the classical frameworks, NN-based models learn the non-linear transforms providing more compact bit represen-tations, and achieve faster coding speed on parallel devices over their classical counterparts. Those properties evoked the attention of both scientific and industrial communities, resulting in the standardization activity JPEG-AI. The verification model for the standardization process of JPEG-AI is already in development and has surpassed the advanced VVC intra codec. To generate reconstructed images with the desired bits per pixel and assess the BD-rate performance of both the JPEG-AI verification model and VVC intra, bit rate matching is employed. However, the current state of the JPEG-AI verification model experiences significant slowdowns during bit rate matching, resulting in suboptimal performance due to an unsuitable model. The proposed methodology offers a gradual algorithmic optimization for matching bit rates, resulting in a fourfold acceleration and over 1% improvement in BD-rate at the base operation point. At the high operation point, the acceleration increases up to sixfold.
APA:
Jia, P., Koyuncu, A.B., Mao, J., Cui, Z., Ma, Y., Guo, T.,... Kaup, A. (2024). Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model. In 2024 Picture Coding Symposium, PCS 2024 - Proceedings. Taichung, TWN: Institute of Electrical and Electronics Engineers Inc..
MLA:
Jia, Panqi, et al. "Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model." Proceedings of the 2024 Picture Coding Symposium, PCS 2024, Taichung, TWN Institute of Electrical and Electronics Engineers Inc., 2024.
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