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Subject: Re: Embedded Image Resizing using FFT
Date: Sun, 23 Nov 2008 10:47:01 +0000 (UTC)
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"Matt" <mjacobson.removethis@xorantech.com> wrote in message <gg71ql$57t$1@fred.mathworks.com>...
> "Oliver Woodford" <o.j.woodford.98@cantab.net> wrote in message <gg6qrh$afk$1@fred.mathworks.com>...
> > "Matt" wrote:
> > > As I was saying, the technique is a combintion of sinc-interpolation and subsampling operations...
> > 
> > I agree. However, sinc filters have long tails, so using them can be slow, and slower still the more you want to reduce your image (as the filters get larger). 
> 
> That's true, however, it is known that linear combinations of sincs can be well approximated by linear combinations of cubic B-splines, which are of small support and faster to manipulate. This is one reason why cubic B-splines are so popular -- they provide a computationally efficient way of aproximating sinc interpolation.

Would blockprocessing make a difference in performance?
e.g. img_dft = blkproc(img,[8 8],@fft2);

Too bad block processing isn't supported in the Embedded MATLAB subset.
And also not sure how I'd add a scaling factor.

If I were to start working on a cubic B-splines algorithm would it be possible to create one in the Embedded MATLAB subset?