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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."
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.
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.