In this proposal, we design a learned multi-frequency image compression approach that uses generalized octave convolutions to factorize the latent representations into high-frequency (HF) and low-frequency (LF) components, and the LF components have lower resolution than HF components, which can improve the rate-distortion performance, similar to wavelet transform. Moreover, compared to the original octave convolution, the proposed generalized octave convolution (GoConv) and octave transposed-convolution (GoTConv) with internal activation layers preserve more spatial structure of the information, and enable more effective filtering between the HF and LF components, which further improve the performance. In addition, we develop a variable-rate scheme using the Lagrangian parameter to modulate all the internal feature maps in the auto-encoder, which allows the scheme to achieve the large bitrate range of the JPEG AI with only three models. Experiments show that the proposed scheme achieves much better Y MS-SSIM than VVC. In terms of YUV PSNR, our scheme is very similar to HEVC.
@article{arxiv.2009.13074,
title = {Learned Variable-Rate Multi-Frequency Image Compression using Modulated Generalized Octave Convolution},
author = {Jianping Lin and Mohammad Akbari and Haisheng Fu and Qian Zhang and Shang Wang and Jie Liang and Dong Liu and Feng Liang and Guohe Zhang and Chengjie Tu},
journal= {arXiv preprint arXiv:2009.13074},
year = {2020}
}
Comments
MMSP 2020; JPEG-AI. arXiv admin note: text overlap with arXiv:2002.10032