2024/03/09 by Cunhui Dong, Haichuan Ma, Dong, Cunhui +9 · 1 citation
Computer Science · Mathematics · #Artificial intelligence #Artificial neural network #Coding (social sciences) #Computer Vision and Pattern Recognition (cs.CV) #Computer science #FOS: Computer and information sciences #FOS: Electrical engineering #Image (mathematics) #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Mathematics #Speech recognition #Statistics #Wavelet #Wavelet transform #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2403.05937
openalex publication_date 2024/03/09 · openalex created_date 2024/03/13 · openalex updated_date 2026/07/28
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.