  To achieve higher coding efficiency, Versatile Video Coding (VVC) includes
several novel components, but at the expense of increasing decoder
computational complexity. These technologies at a low bit rate often create
contouring and ringing effects on the reconstructed frames and introduce
various blocking artifacts at block boundaries. To suppress those visual
artifacts, the VVC framework supports four post-processing filter operations.
The interoperation of these filters introduces extra signaling bits and
eventually becomes overhead at higher resolution video processing. In this
paper, a novel deep learning-based model is proposed for sample adaptive offset
(SAO) nonlinear filtering operation and substantiated the merits of intra-inter
frame quality enhancement. We introduced a variable filter size multi-scale CNN
(MSCNN) to improve the denoising operation and incorporated strided
deconvolution for further computation improvement. We demonstrated that our
deconvolution model can effectively be trained by leveraging the high-frequency
edge features learned in a parallel fashion using feature fusion and residual
learning. The simulation results demonstrate that the proposed method
outperforms the baseline VVC method in BD-BR, BD-PSNR measurements and achieves
an average of 3.762 % bit rate saving on the standard video test sequences.
