Wavelet-like Transform with Subbands Fusion in Decoupled Structure for Deep Image Compression

Wavelet-like transform, based on convolutional neural network (CNN), is content-adaptive and has made remarkable achievements in end-to-end image compression. However, the subsequent sequential processing of each subband in the entropy module takes a relatively long decoding time, resulting in incon-venience for real-world applications. In this work, for lossy image compression, the wavelet-like transform is transplanted into the prevailing autoencoder structure to enhance the analysis and synthesis transform due to its excellent decomposition capability. The obtained subbands of different frequencies will undergo a hierarchical decorrelation architecture for subband fusion, also called cross fusing module. The specialized treatment will be applied to different subbands according to their spatial resolution to attain a more compact latent representation. In addition, the proposed solution features an architecture that decouples the arithmetic decoding process from the sample prediction process, which significantly reduces the decoding complexity. Experiments on the Kodak test set show that the proposed method achieves −3.04% BD-Rate compared to existing decoupled end-to-end structure in RGB Peak Signal-to-Noise Ratio (PSNR).