Exploring Effective Mask Sampling Modeling for Neural Image Compression
Lin Liu Mingming Zhao Shanxin Yuan Wenlong Lyu Wengang Zhou Houqiang Li Yanfeng Wang Qi Tian
Image compression aims to reduce the information redundancy in images. Most
existing neural image compression methods rely on side information from
hyperprior or context models to eliminate spatial redundancy, but rarely
address the channel redundancy. Inspired by the mask sampling modeling in
recent self-supervised learning methods for natural language processing and
high-level vision, we propose a novel pretraining strategy for neural image
compression. Specifically, Cube Mask Sampling Module (CMSM) is proposed to
apply both spatial and channel mask sampling modeling to image compression in
the pre-training stage. Moreover, to further reduce channel redundancy, we
propose the Learnable Channel Mask Module (LCMM) and the Learnable Channel
Completion Module (LCCM). Our plug-and-play CMSM, LCMM, LCCM modules can apply
to both CNN-based and Transformer-based architectures, significantly reduce the
computational cost, and improve the quality of images. Experiments on the
public Kodak and Tecnick datasets demonstrate that our method achieves
competitive performance with lower computational complexity compared to
state-of-the-art image compression methods.