The basic principles of geometric rigidity state that a triangle is the strongest geometric shape and particularly a right triangle when it comes to fixing points.
Ideally you'll have control points in three corners of your image OR at least three points that form a right triangle whose apexes are close to the extremities of the image.
If you don't then you will get pixel errors. They are also sometimes called residuals.
Mind you a rookie mistake can be to transpose the geographic coordinate pairs when entering the data. That will surely give you pixel errors if the points aren't exactly equidistant and rectangular.
Do us a favour, open the TAB file for this image in a text editor, pixel errors and all, and copy the sections out of the tab file that has your coordinate registration details.
With this we can easily see what you're entering as pixels and geographic coordinates. It will help in the diagnostic.
A point though is that pixel errors in the less than 10 is often quite acceptable given the source data and the accuracy of the scan.
Don't forget the grid line a 1:25000 scale map is about 20m wide, so when you click on the intersection you're clicking in a 20m square. Depending on the scan, maybe 600 dpi, every 24 pixels is 1mm. You'll be able to pick out the centre of the grid so you're smallest resolution will only be about 10 pixels.
I aim for 5 or less.
Here's a bit of theory to help you and others who may find this thread.
The "pixel error" being referred to is a measure of geometric alignment between the points you select on the image AND the geographic coordinates you provide.
Using the smallest number of registration points allowed for a valid registration that will generate a residual, 4 points, here is an example.
Lets say you have the top-left, top-right, bottom-left and bottom-right point coordinates for your image.
Lets say the image represents a 1000m square and is 1000 pixels by 1000 pixels eg 1m pixels. This is for simplicity.
When we register we are comparing the image coordinates for these points and the ACTUAL geographic coordinates for these points and calculating the relationship.
In this ideal image if we selected these exact corners of the image and gave them each the right grid coordinate or residuals would be zero.
Please note image coordinate systems and map coordinate systems are set up differently so don't jump all over me when the following coordinates are listed. Origins for images are top-left and increase going right and down.
eg
Image Map residual(pixel error)
0,0 1000,1000 (top left) 0
1000,0 2000,1000(top right) 0
0,1000 1000,0 (bottom left) 0
1000,1000 2000,0(bottom right) 0
This is because the image is perfectly rectangular and the image coordinates selected are exactly in the corners and the geographic coordinates are exactly rectangular.
OK that's the perfect example.
Lets say your image, during scanning was slightly skew, maybe 2 or 3 degrees clockwise.
To get the same reference points in your image, your image will now be slightly larger, not by much, perhaps 5 or 6 pixels larger all around.
If you perform your registration as above your residuals will be zero again. This is because the geometry between the points is the same even though the image is skew.
But
If during scanning something caused the image to warp or stretch or otherwise change shape then you're going to get serious residuals and there's pretty much nothing you can do to address the issue.
If the image is warped Mapinfo has almost no way of dealing with this other than applying warping factors that often don't give great results.
A simple test it to draw a circle on an image with residuals and then remove the image from the layer list. You'll find the circle will change into an ellipse. Same with squares and rectangles. With poor image registrations these will change into parallolgrams or stretch them out without the influence of the image. This is because the image registration applies a "best fit" warp to your vector data.
The problem with scanned sources is that the scanning may have warped them or, one beauty I found, the original printing process warped the map I was scanning.
In these cases I often use open source tools like GDALwarp to apply my control to the image and create a " rubber sheeted" image that's had the scanning defects substantially removed.