Bit Rate Matching Algorithm Optimization in JPEG-AI Verification Model
Panqi Jia A. Burakhan Koyuncu Jue Mao Ze Cui Yi Ma Tiansheng Guo Timofey Solovyev Alexander Karabutov Yin Zhao Jing Wang Elena Alshina Andre Kaup
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 representations, 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.