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Choosing Better-Quality JPEG Images With Software?
kpoole55 writes "I've been googling for an answer to a question and I'm not making much progress. The problem is image collections, and finding the better of near-duplicate images. There are many programs, free and costly, CLI or GUI oriented, for finding visually similar images — but I'm looking for a next step in the process. It's known that saving the same source image in JPEG format at different quality levels produces different images, the one at the lower quality having more JPEG artifacts. I've been trying to find a method to compare two visually similar JPEG images and select the one with the fewest JPEG artifacts (or the one with the most JPEG artifacts, either will serve.) I also suspect that this is going to be one of those 'Well, of course, how else would you do it? It's so simple.' moments."
Compute the root-mean-square difference between the original image and a gaussian-blurred version?
JPEG tends to soften details and reduce areas of sharp contrast, so the sharper result will probably
be better quality. This is similar to the PSNR metric for image quality.
Bonus: very fast, and can be done by convolution, which optimizes very efficiently.
I don't know about "quality", but frankly it shouldn't be too hard to compare similar images just by doing simple mathematical analysis on the results. I'm only vaguely familiar with image compression, but if a "worse" JPEG image is more blocky, would it be possible to run edge detection to find the most clearly defined blocks that indicates a particular picture is producing "worse" results? That's just one idea, I'm sure people who know the compression better can name many other properties that could easily be measured automatically.
What a computer can't do is tell you if the image is subjectively worse, unless the same metric that the human uses to subjectively judge a picture happens to match the algorithm the computer is using, and even then it could vary by picture to picture. For example, a highly colorful picture might hide the artifacting much better than a picture that features lots of text. While the "blockiness" would be the same mathematically, the subjective human viewing it will notice the artifacts in the text much more.
AntiFA: An abbreviation for Anti First Amendment.
For what it's worth: I remember using Paint Shop Pro 9 a few years ago. It has a function called "Removal of JPEG artifacts" (or similar). I remember being surprised how well it worked. I also remember that PSP has quite good functionality for batch processing. So what you could do is use the "remove artifact" function and look at the difference before/after this function. The image with the bigger difference has to be the one of lower quality.
I am not sure if there is a tool that automatically calculates the difference between two images, but this is a task simple enough to be coded in a few lines (given the right libraries are at hand). For each color channel (RGB) of each pixel, you basically just calculate the square of the difference between the two images. Then you add all these numbers up (all pixels, all color channels). The bigger this number is, the bigger the difference between the images.
Maybe not your push-one-button solution, but should be doable. Just my $0.02.
There was a old story my AI teacher used to share back in college about a military contractor that was developing an AI based IFF (identifcation, friend or foe) system for aircraft.
They trained it using what was, at the time, a vast picture database of every aircraft known. In the lab, they were able to get it down to 99% accurate, with the error favoring 'unknown' as the third option.
So they took it out for a test run. The first night out the system tried firing on anything and everything it could lock on, including ground targets.
This was bad. Horribly bad. But they were certain that there was some sort of equipment failure going on. After all their AI was damn near perfect at ID'ing the targets in the lab, the issues must be up the line somewhere.
So they did a once over of the equipment and couldn't find a problem. Not sure what to do next the team took the system out for another dry run the next day. This time, the system refused to see any ground targets and anything it saw in the air was friendly.
Now this was getting ridiculous, the team was extremely confused. So they did what they should have done the first time around, they did a third test run looking at what the AI was actually 'thinking'.
And promptly discovered the problem. While they had a huge database of images to use, they realized that all their 'friendly' craft had pictures taken during the day, while in flight. All their 'hostile' craft however were pictures that had been taken at night during spy runs or from over head satalite shots.
The AI wasn't keying off the planes, it was keying off whether it was daytime or night time.
I don't know if the above actually ever happened, but my point is, it doesn't matter how many images you seed your database with. Unless you are there to tell it what is an artifact and what is just part of the picture, you are going to end up with horrible results and comical results.