Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
release_5d7h7ec2k5gl3btqk6tyuubd5e
by
Jianwei Li, Yongtao Wang, Haihua Xie, Kai-Kuang Ma
2020
Abstract
Lossy compression brings artifacts into the compressed image and degrades the
visual quality. In recent years, many compression artifacts removal methods
based on convolutional neural network (CNN) have been developed with great
success. However, these methods usually train a model based on one specific
value or a small range of quality factors. Obviously, if the test image's
quality factor does not match to the assumed value range, then degraded
performance will be resulted. With this motivation and further consideration of
practical usage, a highly robust compression artifacts removal network is
proposed in this paper. Our proposed network is a single model approach that
can be trained for handling a wide range of quality factors while consistently
delivering superior or comparable image artifacts removal performance. To
demonstrate, we focus on the JPEG compression with quality factors, ranging
from 1 to 60. Note that a turnkey success of our proposed network lies in the
novel utilization of the quantization tables as part of the training data.
Furthermore, it has two branches in parallel---i.e., the restoration branch and
the global branch. The former effectively removes the local artifacts, such as
ringing artifacts removal. On the other hand, the latter extracts the global
features of the entire image that provides highly instrumental image quality
improvement, especially effective on dealing with the global artifacts, such as
blocking, color shifting. Extensive experimental results performed on color and
grayscale images have clearly demonstrated the effectiveness and efficacy of
our proposed single-model approach on the removal of compression artifacts from
the decoded image.
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