  The Sample Adaptive Offset (SAO) filter has been introduced in HEVC to reduce
general coding and banding artefacts in the reconstructed pictures, in
complement to the De-Blocking Filter (DBF) which reduces artifacts at block
boundaries specifically. The new video compression standard Versatile Video
Coding (VVC) reduces the BD-rate by about 36% at the same reconstruction
quality compared to HEVC. It implements an additional new in-loop Adaptive Loop
Filter (ALF) on top of the DBF and the SAO filter, the latter remaining
unchanged compared to HEVC. However, the relative performance of SAO in VVC has
been lowered significantly. In this paper, it is proposed to revisit the SAO
filter using Neural Networks (NN). The general principles of the SAO are kept,
but the a-priori classification of SAO is replaced with a set of neural
networks that determine which reconstructed samples should be corrected and in
which proportion. Similarly to the original SAO, some parameters are determined
at the encoder side and encoded per CTU. The average BD-rate gain of the
proposed SAO improves VVC by at least 2.3% in Random Access while the overall
complexity is kept relatively small compared to other NN-based methods.
