Learnt Mutual Feature Compression for Machine Vision

Recently, image coding for machines (ICM) has been playing an important role in facilitating intelligent vision tasks. Unfortunately, the existing ICM methods separately compress features at each scale, neglecting the redundancy across multi-scale features. To address this issue, this paper proposes an end-to-end mutual compression framework for the ICM, such that the compression efficiency can be significantly improved by removing the cross-scale redundancy. Specifically, the proposed framework consists of a mutual feature compression network (MFCNet) and a basic feature compression network (BFCNet). The MFCNet predicts large-scale features from basic small-scale features, such that the large amount of bit-rates assigned to compress large-scale features can be saved. Moreover, the BFCNet is proposed to compress small-scale features of high quality by removing spatial and channel-wise redundancy. This guarantees superior performances whilst consuming extremely small amount of bit-rates. The experimental results show that our method achieves 90.10% and 74.97% BD-rate saving against the VVC feature anchor and VVC image anchor that have been recently accepted by the moving picture experts group (MPEG).