  In this paper, we present CAESR, an hybrid learning-based coding approach for
spatial scalability based on the versatile video coding (VVC) standard. Our
framework considers a low-resolution signal encoded with VVC intra-mode as a
base-layer (BL), and a deep conditional autoencoder with hyperprior (AE-HP) as
an enhancement-layer (EL) model. The EL encoder takes as inputs both the
upscaled BL reconstruction and the original image. Our approach relies on
conditional coding that learns the optimal mixture of the source and the
upscaled BL image, enabling better performance than residual coding. On the
decoder side, a super-resolution (SR) module is used to recover high-resolution
details and invert the conditional coding process. Experimental results have
shown that our solution is competitive with the VVC full-resolution intra
coding while being scalable.
