Deep Scalable Image Compression via Hierarchical Feature Decorrelation

Scalable image compression allows reconstructing complete images through partially decoding. It plays an important role for image transmission and storage. In this paper, we study the problem of feature decorrelation for Deep Neural Network (DNN) based image codec. Inspired by self-attention mechanism [1], we design a transformer-based decorrelation unit (DU) and adopt it in our scalable image compression framework to reduce the redundancy of feature representations at different levels. Experimental results demonstrate that proposed framework outperforms the state-of-the-art DNN-based scalable image codec and conventional scalable image codecs in terms of MS-SSIM. We also conduct ablation experiments which explicitly verify the effectiveness of decorrelation unit in our scheme.