  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 maximizing HR
reconstruction quality. Then, an efficient frequency-aware decoder reconstructs
a high-fidelity HR image from the LR one in real time. Extensive experiments
demonstrate that our framework outperforms previous image rescaling baselines
in rate-distortion performance and can perform 6K image reconstruction in real
time.
