DIRECT: Discrete Image Rescaling with Enhancement from Case-specific Textures

Yan An Chen, Ching Chun Hsiao, Wen-Hsiao Peng, Ching-Chun Huang

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

This paper addresses image rescaling, the task of which is to downscale an input image followed by upscaling for the purposes of transmission, storage, or playback on heterogeneous devices. The state-of-the-art image rescaling network (known as IRN) tackles image downscaling and upscaling as mutually invertible tasks using invertible affine coupling layers. In particular, for upscaling, IRN models the missing high-frequency component by an input-independent (case-agnostic) Gaussian noise. In this work, we take one step further to predict a case-specific high-frequency component from textures embedded in the downscaled image. Moreover, we adopt integer coupling layers to avoid quantizing the downscaled image. When tested on commonly used datasets, the proposed method, termed DIRECT, improves high-resolution reconstruction quality both subjectively and objectively, while maintaining visually pleasing downscaled images.

原文English
主出版物標題2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728185514
DOIs
出版狀態Published - 5 12月 2021
事件2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Munich, Germany
持續時間: 5 12月 20218 12月 2021

出版系列

名字2021 International Conference on Visual Communications and Image Processing, VCIP 2021 - Proceedings

Conference

Conference2021 International Conference on Visual Communications and Image Processing, VCIP 2021
國家/地區Germany
城市Munich
期間5/12/218/12/21

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