  This paper presents a deep learning-based video compression framework
(ViSTRA3). The proposed framework intelligently adapts video format parameters
of the input video before encoding, subsequently employing a CNN at the decoder
to restore their original format and enhance reconstruction quality. ViSTRA3
has been integrated with the H.266/VVC Test Model VTM 14.0, and evaluated under
the Joint Video Exploration Team Common Test Conditions. Bj{\o}negaard Delta
(BD) measurement results show that the proposed framework consistently
outperforms the original VVC VTM, with average BD-rate savings of 1.8% and 3.7%
based on the assessment of PSNR and VMAF.
