  Spatial frequency analysis and transforms serve a central role in most
engineered image and video lossy codecs, but are rarely employed in neural
network (NN)-based approaches. We propose a novel NN-based image coding
framework that utilizes forward wavelet transforms to decompose the input
signal by spatial frequency. Our encoder generates separate bitstreams for each
latent representation of low and high frequencies. This enables our decoder to
selectively decode bitstreams in a quality-scalable manner. Hence, the decoder
can produce an enhanced image by using an enhancement bitstream in addition to
the base bitstream. Furthermore, our method is able to enhance only a specific
region of interest (ROI) by using a corresponding part of the enhancement
latent representation. Our experiments demonstrate that the proposed method
shows competitive rate-distortion performance compared to several non-scalable
image codecs. We also showcase the effectiveness of our two-level quality
scalability, as well as its practicality in ROI quality enhancement.

Image and Video Processing