Presentation

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Exploration of Super-Resolution Techniques for Image Compression

SessionResearch, ACM SRC, and Doctoral Showcase Posters

DescriptionTEZIP is a (de)compression framework leveraging PredNet, a deep neural network designed for video prediction tasks, to exploit temporal locality in time-evolving data. This study evaluates video super-resolution (VSR) models, which enhance low-resolution images by reconstructing high-resolution ones, under various compression and size reduction techniques. Specifically, we evaluate the VRT and BasicVSR++ models across various compression techniques, including H.264 and H.265, applied to the Vimeo90K dataset. Our results, evaluated using common super-resolution image quality metrics, indicate that the VRT model consistently outperforms BasicVSR++, particularly with H.264 and H.265 compressions. We observe that larger file sizes and lower compression ratios correlate with higher PSNR and SSIM values, highlighting the trade-offs between compression techniques and quality metrics in generating high-resolution images. These findings emphasize the balance needed between compression efficiency and image quality in VSR applications.

Authors

Alexis Amoyo

RIKEN

Florida State University

Amarjit Singh

RIKEN