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.

翻译：当代图像缩放旨在将高分辨率图像嵌入到包含重建信息的低分辨率缩略图中。与传统图像超分辨率不同，该方法可利用低分辨率缩略图中的嵌入信息实现与原始图像高度一致的高保真高分辨率图像恢复。然而，现有先进图像缩放方法未针对高效共享优化低分辨率图像文件大小，且在超高清（如6K）图像重建中无法达到实时性能。为解决这两个挑战，我们提出了一种新型框架（HyperThumbnail），用于实时6K率失真感知图像缩放。该框架首先通过编码器与所提出的量化预测模块，将高分辨率图像嵌入JPEG低分辨率缩略图，在最大化高分辨率重建质量的同时最小化嵌入低分辨率JPEG缩略图的文件大小。随后，高效的频率感知解码器从低分辨率图像中实时重建高保真高分辨率图像。大量实验表明，本框架在率失真性能上优于先前图像缩放基线，并能够实时完成6K图像重建。