Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model - Jia, Panqi; Koyuncu, A. Burakhan; Mao, Jue; Cui, Ze; Ma, Yi; Guo, Tiansheng; Solovyev, Timofey; Karabutov, Alexander; Zhao, Yin; Wang, Jing; Alshina, Elena; Kaup, André (2024 Picture Coding Symposium, PCS 2024 - Proceedings, 2024)
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.
CRIS-Team Scopus Importer:2024-07-19
Keywords: algorithm optimization; bit rate matching; JPEG-AI; Learned Image Compression

LEARNING-BASED CONDITIONAL IMAGE CODER USING COLOR SEPARATION - Jia, Panqi; Koyuncu, Ahmet Burakhan; Gaikov, Georgii; Karabutov, Alexander; Alshina, Elena; Kaup, André (2022 PICTURE CODING SYMPOSIUM (PCS), 2022)
CRIS-Team WoS Importer:2023-03-03