  We present a new video compression framework (ViSTRA2) which exploits
adaptation of spatial resolution and effective bit depth, down-sampling these
parameters at the encoder based on perceptual criteria, and up-sampling at the
decoder using a deep convolution neural network. ViSTRA2 has been integrated
with the reference software of both the HEVC (HM 16.20) and VVC (VTM 4.01), and
evaluated under the Joint Video Exploration Team Common Test Conditions using
the Random Access configuration. Our results show consistent and significant
compression gains against HM and VVC based on Bj{\o}negaard Delta measurements,
with average BD-rate savings of 12.6% (PSNR) and 19.5% (VMAF) over HM and 5.5%
(PSNR) and 8.6% (VMAF) over VTM.
