Wavelet-Like Transform-Based Technology in Response to the Call for Proposals on Neural Network-Based Image Coding
<|reference_start|>Wavelet-Like Transform-Based Technology in Response to the Call for Proposals on Neural Network-Based Image Coding: 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.<|reference_end|>
@article{dong2024wavelet-like,
    title={Wavelet-Like Transform-Based Technology in Response to the Call for
  Proposals on Neural Network-Based Image Coding},
    author={Cunhui Dong, Haichuan Ma, Haotian Zhang, Changsheng Gao, Li Li, Dong
  Liu},
    journal={arXiv preprint arXiv:2403.05937},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.05937},
    primaryClass={cs.CV eess.IV}
}