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.

基于神经网络的图像编码已经取得了迅猛发展，本文介绍了一种新的基于类小波转换的端到端图像编码框架——iWaveV3，它包括诸多新特性，并在客观质量上实现了最先进的压缩效率，在感知质量上具有很强的竞争力。因此，iWaveV3被选定作为开发基于神经网络的图像编码IEEE标准的候选方案。

基于小波样变换技术的神经网络图像编码提案响应