  In video coding, in-loop filters are applied on reconstructed video frames to
enhance their perceptual quality, before storing the frames for output.
Conventional in-loop filters are obtained by hand-crafted methods. Recently,
learned filters based on convolutional neural networks that utilize attention
mechanisms have been shown to improve upon traditional techniques. However,
these solutions are typically significantly more computationally expensive,
limiting their potential for practical applications. The proposed method uses a
novel combination of sparsity and structured pruning for complexity reduction
of learned in-loop filters. This is done through a three-step training process
of magnitude-guidedweight pruning, insignificant neuron identification and
removal, and fine-tuning. Through initial tests we find that network parameters
can be significantly reduced with a minimal impact on network performance.
