Selective compression learning of latent representations for
variable-rate image compression
- URL: http://arxiv.org/abs/2211.04104v1
- Date: Tue, 8 Nov 2022 09:09:59 GMT
- Title: Selective compression learning of latent representations for
variable-rate image compression
- Authors: Jooyoung Lee and Seyoon Jeong and Munchurl Kim
- Abstract summary: We propose a selective compression method that partially encodes latent representations in a fully generalized manner for deep learning-based variable-rate image compression.
The proposed method can achieve comparable compression efficiency as those of the separately trained reference compression models and can reduce decoding time owing to the selective compression.
- Score: 38.077284943341105
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Recently, many neural network-based image compression methods have shown
promising results superior to the existing tool-based conventional codecs.
However, most of them are often trained as separate models for different target
bit rates, thus increasing the model complexity. Therefore, several studies
have been conducted for learned compression that supports variable rates with
single models, but they require additional network modules, layers, or inputs
that often lead to complexity overhead, or do not provide sufficient coding
efficiency. In this paper, we firstly propose a selective compression method
that partially encodes the latent representations in a fully generalized manner
for deep learning-based variable-rate image compression. The proposed method
adaptively determines essential representation elements for compression of
different target quality levels. For this, we first generate a 3D importance
map as the nature of input content to represent the underlying importance of
the representation elements. The 3D importance map is then adjusted for
different target quality levels using importance adjustment curves. The
adjusted 3D importance map is finally converted into a 3D binary mask to
determine the essential representation elements for compression. The proposed
method can be easily integrated with the existing compression models with a
negligible amount of overhead increase. Our method can also enable continuously
variable-rate compression via simple interpolation of the importance adjustment
curves among different quality levels. The extensive experimental results show
that the proposed method can achieve comparable compression efficiency as those
of the separately trained reference compression models and can reduce decoding
time owing to the selective compression. The sample codes are publicly
available at https://github.com/JooyoungLeeETRI/SCR.
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