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A Low-complexity Neural Network for Compressed Video Post-processing in HEVC

Citation Author(s):
Zheng Liu, Honggang Qi, Yu han, Guoqin Cui, Yundong Zhang
Submitted by:
Zheng Liu
Last updated:
26 February 2022 - 8:53am
Document Type:
Document Year:
Zheng Liu
Paper Code:

Video post-processing is a method to improve the quality of reconstructed frames at the
decoder side. Although the existing post-processing algorithms based on deep learning
can achieve signi cant quality improvement compared with traditional methods, they will
require a lot of computational resources, which makes these algorithms difficult to use
on mobile devices. To tackle this problem, a low-complexity neural network based on
max-pooling and depth-wise separable convolution is proposed in this work for compressed
video post-processing. Max-pooling can signi cantly reduce memory consumption, and
depth-wise separable convolution can reduce the computational resources required by the
model. Experiment results show that compared with the existing methods, our model
can signi cantly improve the processing speed with few parameters under almost the same
quality improvement.

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