  Image downscaling is one of the widely used operations in image processing
and computer graphics. It was recently demonstrated in the literature that
kernel-based convolutional filters could be modified to develop efficient image
downscaling algorithms. In this work, we present a new downscaling technique
which is based on kernel-based image filtering concept. We propose to use
pairwise co-occurrence similarity of the pixelpairs as the range kernel
similarity in the filtering operation. The co-occurrence of the pixel-pair is
learned directly from the input image. This co-occurrence learning is performed
in a neighborhood based fashion all over the image. The proposed method can
preserve the high-frequency structures, which were present in the input image,
into the downscaled image. The resulting images retain visually important
details and do not suffer from edge-blurring artifact. We demonstrate the
effectiveness of our proposed approach with extensive experiments on a large
number of images downscaled with various downscaling factors.
