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<title>Learning to Hide Residual for Boosting Image Compression</title>

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<h1>Learning to Hide Residual for Boosting Image Compression</h1>
<div class="authors">
<a href="https://yilunlee.github.io/" target="_blank">Yi-Lun Lee</a>,
<a href="https://yenchungchen.github.io/" target="_blank">Yen-Chung Chen</a>,
<a href="mailto:piews482zt@gmail.com" target="_blank">Min-Yuan Tseng</a>,
<a href="https://sites.google.com/site/yihsuantsai/" target="_blank">Yi-Hsuan Tsai</a>,
<a href="https://walonchiu.github.io/" target="_blank">Wei-Chen Chiu</a>
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<p style="text-align:center;">
National Yang Ming Chiao Tung University, Taiwan
<br>
Phiar Technologies, Inc
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<h2>Abstract</h2>
<p style="text-align: justify;">
Lossy compression usually leads to severe compression artifacts, such as blocking boundary, mosquito noise, and blur. Reducing compression artifacts is essential for better visual experience and quality when transmitting data under limited bandwidth, where the sender compresses an image and transmits it via a communication channel to the receiver side. To tackle this problem, most existing methods aim to directly recover details from received compressed image, instead of fully exploiting the rich information contained in the uncompressed image. In this paper, we focus on leveraging the residual information, i.e. the difference between a compressed image and its corresponding original/uncompressed one, and propose to hide the residual into the original image by a novel framework. As such, our model that considers this resultant image with the hidden information has a better ability to recover the residual caused by the compression process. Afterwards, the hidden residual could be decoded from the received image and used to boost the quality of image reconstruction on the receiver side. Extensive experiments verify the efficacy of our proposed framework in reducing compression artifacts and showing favorable performance against numerous baselines.
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<h2>Method</h2>
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<h2>Quantitative Results</h2>
<center><h5>Traditional Codecs</h5></center>
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<center><p><br></p></center>

<center><h5>Deep-learning-based Codecs</h5></center>
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<h2>Qualitative Results</h2>
<center><h5>Kinetics</h5></center>
<img src="static/fig/qualitative_kinetics.png" width="100%">
<center><p><br></p></center>

<center><h5>Kodak</h5></center>
<img src="static/fig/qualitative_kodak.png" width="100%">

<!-- <h3>Generation</h3>
<img src="static/fig/sample.jpg" width="100%">
<center><p>Qualitative examples of the point clouds generated by our proposed recursive point cloud generator (RPG).</p></center>
<h3>Interpolation</h3>
<img src="static/fig/interpolation.jpg" width="100%">
<center><p>Examples for our interpolation between different shapes: (a) Rows sequentially show the point clouds generated on all the expansion stages while interpolating between the chairs on the bottom-left and bottom-right corners; (b) Each row shows interpolation between two 3D shapes of the same object category; (c) Each row shows interpolation between two shapes from different categories.</p></center>
<h3>Co-segmentation</h3>
<img src="static/fig/co-segmentation.jpg" width="100%">
<center><p>Visualization of co-segmentation results among object instances from Car, Chair and Airplane categories in ShapeNet. For each object category, the rows sequentially highlight different common parts with green color shared across the instances.</p></center>
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<h3>Citation</h3>
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<pre>@misc{ko2021rpg,
title={RPG: Learning Recursive Point Cloud Generation},
author={Wei-Jan Ko and Hui-Yu Huang and Yu-Liang Kuo and Chen-Yi Chiu and Li-Heng Wang and Wei-Chen Chiu},
year={2021},
eprint={2105.14322},
archivePrefix={arXiv},
primaryClass={cs.CV}}</pre>
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