Bit Distribution Study and Implementation of Spatial Quality Map in the
  JPEG-AI Standardization
Panqi Jia Jue Mao Esin Koyuncu A. Burakhan Koyuncu Timofey Solovyev Alexander Karabutov Yin Zhao Elena Alshina Andre Kaup
Currently, there is a high demand for neural network-based image compression
codecs. These codecs employ non-linear transforms to create compact bit
representations and facilitate faster coding speeds on devices compared to the
hand-crafted transforms used in classical frameworks. The scientific and
industrial communities are highly interested in these properties, leading to
the standardization effort of JPEG-AI. The JPEG-AI verification model has been
released and is currently under development for standardization. Utilizing
neural networks, it can outperform the classic codec VVC intra by over 10%
BD-rate operating at base operation point. Researchers attribute this success
to the flexible bit distribution in the spatial domain, in contrast to VVC
intra's anchor that is generated with a constant quality point. However, our
study reveals that VVC intra displays a more adaptable bit distribution
structure through the implementation of various block sizes. As a result of our
observations, we have proposed a spatial bit allocation method to optimize the
JPEG-AI verification model's bit distribution and enhance the visual quality.
Furthermore, by applying the VVC bit distribution strategy, the objective
performance of JPEG-AI verification mode can be further improved, resulting in
a maximum gain of 0.45 dB in PSNR-Y.