Reputation: 17560
I noticed that of the two methods below for scaling an image N halfs that the first produced a more smooth image, looking more appealing to the eye.
while (lod-- > Payload->MaxZoom)
{
cv::resize(img, img, cv::Size(), 0.5, 0.5, cv::INTER_LINEAR);
}
vs
double scale = 1.0 / (1<< (lod - Payload->MaxZoom));
cv::resize(img, img, cv::Size(), scale, scale, cv::INTER_LINEAR);
I am interested in knowing if there is a interpolation that would produce similar result as the first resize but not having to loop over it N times.
Any mathematical insight into why doing the resize in multiply steps can result in a better result is also interesting.
The latter method above gives a very pixelated result (for N=5) where the first is very smooth (it makes sense since its the average of 4 pixel over N steps)
Upvotes: 4
Views: 3657
Reputation: 4335
This happens because OpenCV's implementation of linear interpolation is rather simplistic.
A simple implementation of linear interpolation takes the values of four pixels closest to the interpolated point and interpolates between them. This is all right for upscaling, but for downscaling, this will ignore the values of many pixels - if there are N pixels in the output image, then it depends on at most 4N pixels of the input. This cannot give good results when the product of scaling factors is lower than 0.25.
The correct thing to do is to consider all input pixels that correspond to an output pixel after the transformation, and compute an average over them (or more generally, compute a convolution with a suitable resampling filter).
OpenCV seems to have an interpolation mode called cv::INTER_AREA
, which should do the thing you want.
Upvotes: 4