Wavelet-Like Transform-Based Technology in Response to the Call for
  Proposals on Neural Network-Based Image Coding
Cunhui Dong Haichuan Ma Haotian Zhang Changsheng Gao Li Li Dong Liu
Neural network-based image coding has been developing rapidly since its
birth. Until 2022, its performance has surpassed that of the best-performing
traditional image coding framework -- H.266/VVC. Witnessing such success, the
IEEE 1857.11 working subgroup initializes a neural network-based image coding
standard project and issues a corresponding call for proposals (CfP). In
response to the CfP, this paper introduces a novel wavelet-like transform-based
end-to-end image coding framework -- iWaveV3. iWaveV3 incorporates many new
features such as affine wavelet-like transform, perceptual-friendly quality
metric, and more advanced training and online optimization strategies into our
previous wavelet-like transform-based framework iWave++. While preserving the
features of supporting lossy and lossless compression simultaneously, iWaveV3
also achieves state-of-the-art compression efficiency for objective quality and
is very competitive for perceptual quality. As a result, iWaveV3 is adopted as
a candidate scheme for developing the IEEE Standard for neural-network-based
image coding.