Abstract: Super-resolution is nowadays used for a high-resolution
image produced from several low-resolution noisy frames. In
this work, we consider the problem of high-quality interpolation of a
single noise-free image. Such images may come from different sources,
i.e., they may be frames of videos, individual pictures, etc. On
the other hand, in the encoder we apply a downsampling via
bidimen-sional interpolation of each frame, and in the decoder we
apply a upsampling by which we restore the original size of the
image. If the compression ratio is very high, then we use a
convolutive mask that restores the edges, eliminating the blur.
Finally, both, the encoder and the complete decoder are implemented
on General-Purpose computation on Graphics Processing Units
(GPGPU) cards. In fact, the mentioned mask is coded inside texture
memory of a GPGPU.
Abstract: In this work, we developed the concept of
supercompression, i.e., compression above the compression standard
used. In this context, both compression rates are multiplied. In fact,
supercompression is based on super-resolution. That is to say,
supercompression is a data compression technique that superpose
spatial image compression on top of bit-per-pixel compression to
achieve very high compression ratios. If the compression ratio is very
high, then we use a convolutive mask inside decoder that restores the
edges, eliminating the blur. Finally, both, the encoder and the
complete decoder are implemented on General-Purpose computation
on Graphics Processing Units (GPGPU) cards. Specifically, the
mentio-ned mask is coded inside texture memory of a GPGPU.