  A rapidly increasing portion of Internet traffic is dominated by requests
from mobile devices with limited- and metered-bandwidth constraints. To satisfy
these requests, it has become standard practice for websites to transmit small
and extremely compressed image previews as part of the initial page-load
process. Recent work, based on an adaptive triangulation of the target image,
has shown the ability to generate thumbnails of full images at extreme
compression rates: 200 bytes or less with impressive gains (in terms of PSNR
and SSIM) over both JPEG and WebP standards. However, qualitative assessments
and preservation of semantic content can be less favorable. We present a novel
method to significantly improve the reconstruction quality of the original
image with no changes to the encoded information. Our neural-based decoding not
only achieves higher PSNR and SSIM scores than the original methods, but also
yields a substantial increase in semantic-level content preservation. In
addition, by keeping the same encoding stream, our solution is completely
inter-operable with the original decoder. The end result is suitable for a
range of small-device deployments, as it involves only a single forward-pass
through a small, scalable network.
