  It has recently been demonstrated that spatial resolution adaptation can be
integrated within video compression to improve overall coding performance by
spatially down-sampling before encoding and super-resolving at the decoder.
Significant improvements have been reported when convolutional neural networks
(CNNs) were used to perform the resolution up-sampling. However, this approach
suffers from high complexity at the decoder due to the employment of CNN-based
super-resolution. In this paper, a novel framework is proposed which supports
the flexible allocation of complexity between the encoder and decoder. This
approach employs a CNN model for video down-sampling at the encoder and uses a
Lanczos3 filter to reconstruct full resolution at the decoder. The proposed
method was integrated into the HEVC HM 16.20 software and evaluated on JVET UHD
test sequences using the All Intra configuration. The experimental results
demonstrate the potential of the proposed approach, with significant bitrate
savings (more than 10%) over the original HEVC HM, coupled with reduced
computational complexity at both encoder (29%) and decoder (10%).
