  Lossy image and video compression algorithms yield visually annoying
artifacts including blocking, blurring, and ringing, especially at low
bit-rates. To reduce these artifacts, post-processing techniques have been
extensively studied. Recently, inspired by the great success of convolutional
neural network (CNN) in computer vision, some researches were performed on
adopting CNN in post-processing, mostly for JPEG compressed images. In this
paper, we present a CNN-based post-processing algorithm for High Efficiency
Video Coding (HEVC), the state-of-the-art video coding standard. We redesign a
Variable-filter-size Residue-learning CNN (VRCNN) to improve the performance
and to accelerate network training. Experimental results show that using our
VRCNN as post-processing leads to on average 4.6% bit-rate reduction compared
to HEVC baseline. The VRCNN outperforms previously studied networks in
achieving higher bit-rate reduction, lower memory cost, and multiplied
computational speedup.
