  We present a new image scaling method both for downscaling and upscaling,
running with any scale factor or desired size. The resized image is achieved by
sampling a bivariate polynomial which globally interpolates the data at the new
scale. The method's particularities lay in both the sampling model and the
interpolation polynomial we use. Rather than classical uniform grids, we
consider an unusual sampling system based on Chebyshev zeros of the first kind.
Such optimal distribution of nodes permits to consider near--best interpolation
polynomials defined by a filter of de la Vall\'ee Poussin type. The action ray
of this filter provides an additional parameter that can be suitably regulated
to improve the approximation. The method has been tested on a significant
number of different image datasets. The results are evaluated in qualitative
and quantitative terms and compared with other available competitive methods.
The perceived quality of the resulting scaled images is such that important
details are preserved, and the appearance of artifacts is low. Competitive
quality measurement values, good visual quality, limited computational effort,
and moderate memory demand make the method suitable for real-world
applications.
