COMPASS: High-Efficiency Deep Image Compression with Arbitrary-scale
  Spatial Scalability
Jongmin Park Jooyoung Lee Munchurl Kim
Recently, neural network (NN)-based image compression studies have actively
been made and has shown impressive performance in comparison to traditional
methods. However, most of the works have focused on non-scalable image
compression (single-layer coding) while spatially scalable image compression
has drawn less attention although it has many applications. In this paper, we
propose a novel NN-based spatially scalable image compression method, called
COMPASS, which supports arbitrary-scale spatial scalability. Our proposed
COMPASS has a very flexible structure where the number of layers and their
respective scale factors can be arbitrarily determined during inference. To
reduce the spatial redundancy between adjacent layers for arbitrary scale
factors, our COMPASS adopts an inter-layer arbitrary scale prediction method,
called LIFF, based on implicit neural representation. We propose a combined RD
loss function to effectively train multiple layers. Experimental results show
that our COMPASS achieves BD-rate gain of -58.33% and -47.17% at maximum
compared to SHVC and the state-of-the-art NN-based spatially scalable image
compression method, respectively, for various combinations of scale factors.
Our COMPASS also shows comparable or even better coding efficiency than the
single-layer coding for various scale factors.