Sandwiched image compression: Increasing the resolution and dynamic range of standard codecs
Neural pre- and post-processing for compressing higher-resolution and HDR images.
Given a standard image codec, we compress images that may have higher resolution and/or higher bit depth
than allowed in the codec's specifications, by sandwiching the standard codec between a neural
pre-processor (before the standard encoder) and a neural post-processor (after the standard decoder).
Using a differentiable proxy for the the standard codec, we design the neural pre- and post-processors to
transport the high resolution (super-resolution, SR) or high bit depth (high dynamic range, HDR) images as
lower resolution and lower bit depth images. The neural processors accomplish this with spatially coded
modulation, which acts as watermarks to preserve the important image detail during compression.
Experiments show that compared to conventional methods of transmitting high resolution or high bit depth
through lower resolution or lower bit depth codecs, our sandwich architecture gains ~9 dB for SR images and
~3 dB for HDR images at the same rate over large test sets. We also observe significant gains in visual
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