Hierarchical Feature Aggregation Network for Deep Image Compression

Abstract

Existing CNN-based methods for image compression extract features through serially connected high-to-low (encoder) or low-to-high (decoder) resolution stages, leading to insufficient utilization of hierarchical features. To solve this problem, we present a hierarchical feature aggregation network (HFAN) for generating more informative latent representations. In detail, we propose two strategies, namely inter-stage feature aggregation and intra-stage feature aggregation. The inter-stage feature aggregation integrates multi-scale information thereby producing more contextual features. The intra-stage aggregation fuses features within the same stage to enrich representations of one specific resolution. Besides, we incorporate a lightweight pixel-wise attention mechanism to further enhance the discriminative ability of our network. Extensive experiments demonstrate that our HFAN achieves superior performance over state-of-the-art methods without a hyperprior variational autoencoder.