Real-time 6K Image Rescaling with Rate-distortion Optimization
Chenyang Qi, Xin Yang, Ka Leong Cheng, Ying-Cong Chen, Qifeng Chen

TL;DR
This paper introduces HyperThumbnail, a real-time framework for 6K image rescaling that optimizes file size and reconstruction quality by embedding high-resolution images into JPEG thumbnails with a novel encoder and frequency-aware decoder.
Contribution
The paper presents a novel rate-distortion-aware image rescaling framework that enables real-time 6K image reconstruction with optimized embedding and decoding techniques.
Findings
Outperforms previous methods in rate-distortion performance.
Achieves real-time 6K image reconstruction.
Efficiently embeds high-resolution images into JPEG thumbnails.
Abstract
Contemporary image rescaling aims at embedding a high-resolution (HR) image into a low-resolution (LR) thumbnail image that contains embedded information for HR image reconstruction. Unlike traditional image super-resolution, this enables high-fidelity HR image restoration faithful to the original one, given the embedded information in the LR thumbnail. However, state-of-the-art image rescaling methods do not optimize the LR image file size for efficient sharing and fall short of real-time performance for ultra-high-resolution (e.g., 6K) image reconstruction. To address these two challenges, we propose a novel framework (HyperThumbnail) for real-time 6K rate-distortion-aware image rescaling. Our framework first embeds an HR image into a JPEG LR thumbnail by an encoder with our proposed quantization prediction module, which minimizes the file size of the embedding LR JPEG thumbnail while…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image Processing Techniques and Applications · Image and Signal Denoising Methods
