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Photo ReSizing with ACDSee Ultimate 8

Photo ReSizing with ACDSee Ultimate 8

Above, the first test photo

For me, one feature I use on virtually EVERY photo is resize.  I dont print much any more, and I never print anything that wouldnt fit comfortably on the typical home wall.  So the resize option of Enlarge isnt something I care that much about.  The default output of my m43s camera, the E-M10, handles those print sizes quite readily.

However, I DO post to the internet photo sharing sites like Flickr & Google, and the photo technology sites like Digital Photography Review.  I have a continual need to downsize my photos for easy upload, and to not cause a display bottleneck when people want to view my photos.

So resizing to a smaller image size is something I do quite a bit.  And my current favorite software, ACDSee Ultimate 8 (and ACDSee Pro 8 and ACDSee 18) offers no less than eight (8 !!!) resampling methods.  

Above, the second test photo

They are, in the order listed by ACDSee:

1. Box
2. Triangle
3. Bi Cubic
4. Bell
5. B-Spline
6. Lanczos
7. Mitchell
8. ClearIQZ

The default resampling method appears to be Lanczos.

ACDSee also offers some good advice for resampling in the ACDSee Ultimate 8 Help file.

A Screen Print directly from the help file.  Get to KNOW it!

I began to wonder, is the default sampling method of Lanczos any good?  Why is it the default? What do the other methods do?  And are there times when I should be using something else?  So I did some research and some testing both with down sizing and with up sizing.

It turns out this is not the simple try them all and pick one sort of test.  My research led to an enormous number of scholarly works complete with mathematical formulae and other thinky stuff.  Image resizing will remain bewildering, Im afraid!

I tried to cherry pick the stuff that seemed relevant to some dumb-ass photographer,  and when I boiled it down, my descriptions seemed to look pretty much like the descriptions found in the ACDSee help file.  So to save my typing Ill just show you a screen print of that section in the help file.

However, there are some interesting notes that ACDSee didnt put in there.  Ill list them for you.

• Box - When reducing image size, it averages and merges the sampled pixels together. So the smaller the reduction, the more pixels will be averaged together. 
• Triangle - As near as I can tell, this is the same thing as Bilinear.  Im not sure what the name change denotes.  If so, This will cause a situation where the closer a sampled pixel is to the source pixel, the stronger influence on color the sampled pixel will have.  As a result, we can expect to see a more . . . global averaging of color (i.e. some color shift, some times)
• Bi Cubic - Nothing to add.  It does its job, I guess. 
• Bell - It can result in a high degree of blurring. If Ive read those research papers correctly, it sounds as if the mathematical algorithm used in this resampling technique might also be used in noise control as well.
• B-Spline - Seems to be used a lot in animation for a smoother flow.
• Lanczos - can cause a haloing effect around edges, but considering how important edge sharpness is in human vision, this is not automatically considered a negative.
• Mitchell - Seems to be related to Lanczos.  Consensus seems to be that it is good for enlarging photos.
• ClearIQZ - I cant find anything on this, it seems to be unique to ACDSee.  It is possibly a proprietary feature of ACDSee, I dont know.  I did find some years old forum posts from a variety of sources that seem to be quite enthusiastic about its ability to enlarge photos.

Edit added 4/8/2015 to clarify sizing terminology: On my first test, I used a slightly noisy, available light photo.I reduced the image to 66%. 

It might be a good time to take note of ACDSees resizing terminology.  When I select a percentage that I want to reduce the image size TO, for example, 66%, I am saying that the new image size should be 66% of the size of the original.  I am not saying that I should reduce the image size BY 66%.   This means a 66% reduction in size will be larger than than a 33% reduction in size.  Conversely, an enlargement to 133% results in a smaller image than an enlargement to 166%.  At times, I have found it easy to confuse even myself as to what I have told ACDSee to do. End of Edit

Below, is a comparison of the 3 methods I judged the best reduction results:

It may not be clear at the small size of this photo, but I judged the Box sampling method to have yielded the best reduction results, over all.  My reasoning was, while all the methods resulted in some blurring, I felt Box did the best at retaining the flavor of the noise pattern and that resulted in a slightly greater illusion of detail.

However, this is not to say that Box is the clear winner.  I wondered if a more conventionally exposed image would yield the same results. So I selected a close up image taken outside in bright sunlight.  I figured this would help determine just how good detail is retained in a photo and would help with judging any color shift that might occur.

The good news is, that none of the methods resulted in significant color shift, though the better results tended to add more contrast to the image, which can shift our perception of a difference in color.

Below is a screen shot comparing the 3 reduction methods I judged the best. These are also reduced to 66%.

Here, I felt Lanczos did the best, though the differences in quality of the three are minimal.  Mitchell was probably closest in terms of color rendition, but I felt the slight added contrast to the Lanczos version tipped the detail retention to Lanczos (but just barely).  None of them are "bad" though.  If your photo requires a bit more color accuracy, you might want to consider Mitchell.  I felt ClearIQZ did a fine job but didnt really do any better than Lanczos.

But What About Enlarging?

My Analysis

Posted by Unknown at 4:51 AM

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