In digital publishing, a low-resolution image is highly undesirable.
Inexperienced users often try to include low-resolution
images from the Internet or digital cameras in documents they are
composing. Current preflight tools are able to single them out, but
what if those low-resolution images have been interpolated? They
may have a sufficient resolution, but their quality has been compromised,
especially images interpolated by nearest-neighbor (which
includes pixel replication) and bilinear interpolation. The interpolated
images often display blocky artifacts, blurry artifacts, or loss of texture.
We outline novel nearest-neighbor and bilinear interpolation
detection algorithms that are designed to estimate rational resampling
factors (above 1) in both the vertical and horizontal dimensions.
The robustness of these algorithms to several common postprocessing
algorithms is also evaluated.
In digital publishing, a low-resolution image is highly undesirable. Inexperienced users often try to include lowresolution images from the Internet or digital cameras in documents they are composing. Current preflight tools are able to single them out, but what if those low-resolution images have been interpolated? They may have a sufficient resolution, but their quality has been compromised, especially images interpolated by nearest-neighbor (which includes pixel replication) and bilinear interpolation. The interpolated images often display blocky artifacts, blurry artifacts, or loss of texture. In this paper, we outline novel nearest-neighbor and bilinear interpolation detection algorithms that are designed to estimate rational resampling factors (above 1×) in both the vertical and horizontal dimensions. The robustness of these algorithms to several common post-processing algorithms is also evaluated.
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