Real-Time 6K Image Rescaling With Rate-Distortion Optimization
Chenyang Qi, Xin Yang, Ka Leong Cheng, Ying-Cong Chen, Qifeng Chen
Abstract
The task of image rescaling aims at embedding an high-resolution (HR) image into a low-resolution (LR) one that can contain embedded information for HR image reconstruction. Existing image rescaling methods do not optimize the LR image file size and recent flow-based rescaling methods are not real-time yet for HR image reconstruction (e.g., 6K). To address these two challenges, we propose a novel framework (HyperThumbnail) for real-time 6K rate-distortion-aware image rescaling. Our HyperThumbnail first embeds an HR image into a JPEG LR image (thumbnail) by an encoder with our proposed learnable JPEG quantization module, which optimizes the file size of the embedding LR JPEG image. Then, an efficient decoder reconstructs a high-fidelity HR (6K) image from the LR one in real time. Extensive experiments demonstrate that our framework outperforms previous image rescaling baselines in both rate-distortion performance and is much faster than prior work in HR image reconstruction speed.
BibTeX
@inproceedings{cvpr2023_realtime6kimager,
title = {Real-Time 6K Image Rescaling With Rate-Distortion Optimization},
author = {Chenyang Qi and Xin Yang and Ka Leong Cheng and Ying-Cong Chen and Qifeng Chen},
booktitle = {CVPR 2023},
year = {2023}
}