  We propose a framework for learned image and video compression using the
generative sparse visual representation (SVR) guided by fidelity-preserving
controls. By embedding inputs into a discrete latent space spanned by learned
visual codebooks, SVR-based compression transmits integer codeword indices,
which is efficient and cross-platform robust. However, high-quality (HQ)
reconstruction in the decoder relies on intermediate feature inputs from the
encoder via direct connections. Due to the prohibitively high transmission
costs, previous SVR-based compression methods remove such feature links,
resulting in largely degraded reconstruction quality. In this work, we treat
the intermediate features as fidelity-preserving control signals that guide the
conditioned generative reconstruction in the decoder. Instead of discarding or
directly transferring such signals, we draw them from a low-quality (LQ)
fidelity-preserving alternative input that is sent to the decoder with very low
bitrate. These control signals provide complementary fidelity cues to improve
reconstruction, and their quality is determined by the compression rate of the
LQ alternative, which can be tuned to trade off bitrate, fidelity and
perceptual quality. Our framework can be conveniently used for both learned
image compression (LIC) and learned video compression (LVC). Since SVR is
robust against input perturbations, a large portion of codeword indices between
adjacent frames can be the same. By only transferring different indices,
SVR-based LIC and LVC can share a similar processing pipeline. Experiments over
standard image and video compression benchmarks demonstrate the effectiveness
of our approach.
