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Dawson
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This question is AWESOME! ... I think we're all getting too technical though.

100 x 100 pixel image = 10000 total pixels

Scaling an image down pulls pixels out. Scaling up adds them. Either way the software takes an "educated guess" as to alter the file.

A single reduction: 90 x 90 (1900px removed from the original file information)

2 Step reduction: 95 x 95 (975px removed), 90 x 90 (another 925). The detail to catch here is that of the total 1900px removed - 975 of them were NOT part of the original information.

The original image is always the best. Fewer "generations" always equates to better quality (closest to the original quality).

PROOF (and a response to @mutoo's comment)

enter image description here

It's simple...it's an algorith...it's not a set of human eyes. There are 3 colors here. 100% black, 50% black, and white (gray scale image). No matter how I scale it - image size menu, transform tool, RGB, CMYK, 100 x 100px, 10 x 10in, the results are the same:

Along the black/gray edge you find 80% black (a color that doesn't exist). Along the white/gray edge you find 7% black (doesn't exist). [not an invitation for anti-alias argument here]

As we all know (being human, and all), a perfect reduction or enlargement would produce a Black/Gray/White striped box. And I still found that a single iteration (up or down) created a better replica than multiple.

This question is AWESOME! ... I think we're all getting too technical though.

100 x 100 pixel image = 10000 total pixels

Scaling an image down pulls pixels out. Scaling up adds them. Either way the software takes an "educated guess" as to alter the file.

A single reduction: 90 x 90 (1900px removed from the original file information)

2 Step reduction: 95 x 95 (975px removed), 90 x 90 (another 925). The detail to catch here is that of the total 1900px removed - 975 of them were NOT part of the original information.

The original image is always the best. Fewer "generations" always equates to better quality (closest to the original quality).

This question is AWESOME! ... I think we're all getting too technical though.

100 x 100 pixel image = 10000 total pixels

Scaling an image down pulls pixels out. Scaling up adds them. Either way the software takes an "educated guess" as to alter the file.

A single reduction: 90 x 90 (1900px removed from the original file information)

2 Step reduction: 95 x 95 (975px removed), 90 x 90 (another 925). The detail to catch here is that of the total 1900px removed - 975 of them were NOT part of the original information.

The original image is always the best. Fewer "generations" always equates to better quality (closest to the original quality).

PROOF (and a response to @mutoo's comment)

enter image description here

It's simple...it's an algorith...it's not a set of human eyes. There are 3 colors here. 100% black, 50% black, and white (gray scale image). No matter how I scale it - image size menu, transform tool, RGB, CMYK, 100 x 100px, 10 x 10in, the results are the same:

Along the black/gray edge you find 80% black (a color that doesn't exist). Along the white/gray edge you find 7% black (doesn't exist). [not an invitation for anti-alias argument here]

As we all know (being human, and all), a perfect reduction or enlargement would produce a Black/Gray/White striped box. And I still found that a single iteration (up or down) created a better replica than multiple.

Source Link
Dawson
  • 1.1k
  • 5
  • 8

This question is AWESOME! ... I think we're all getting too technical though.

100 x 100 pixel image = 10000 total pixels

Scaling an image down pulls pixels out. Scaling up adds them. Either way the software takes an "educated guess" as to alter the file.

A single reduction: 90 x 90 (1900px removed from the original file information)

2 Step reduction: 95 x 95 (975px removed), 90 x 90 (another 925). The detail to catch here is that of the total 1900px removed - 975 of them were NOT part of the original information.

The original image is always the best. Fewer "generations" always equates to better quality (closest to the original quality).