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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."
The ImageMagick package includes a command called identify, which can read the EXIF data in the JPEG file. You can use it like this:
identify -verbose creek.jpg | grep Quality
In my example, it gave " Quality: 94".
This will not work on very old cameras (from ca. 2002 or earlier?), because they don't have EXIF data. This is different info than you'd get by just comparing file sizes. The JPEG quality setting is not the only factor that can influence file size. File size can depend on resolution, JPEG quality, and other manipulations such as blurring or sharpening, adjusting brightness levels, etc.
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File size doesn't tell you everything about quality.
For instance, if you save an image as a JPEG vs. first saving as a dithered GIF and _then_ saving as JPEG, then the second one will have much worse actual quality, even if it has the same filesize (it may well have worse quality AND have a larger file size).
Run the DCT and check how much it's been quantized. The higher the greatest common factor, the more it has been compressed.
Alternatively, check the raw data file size.
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.
Just look at the manner in which JPEGs are encoded for your answer!
Take the DCT (discrete cosine transform) of blocks of pixels throughout the image. Examine the frequency content of the each of these blocks and determine the amount of spatial frequency suppression. This will correlate with the quality factor used during compression!
load up both images in adobe after effects or some other image compositing program and apply a "difference matte"
Any differences in pixel values between the two images will show up as black on a white background or vise versa...
adam
BOXXlabs
Oh sure, it starts out innocently enough - pick the better image. Next thing you know Skynet's decided that it's the better LIFE-FORM.
AI - JUST SAY NO!
Brought to you by the Coalition for Human Survival (C) Aug. 29, 1997
I mean, you don't want second rate pictures in your pr0n stash?
I had problems building it back then, let alone writing the scripts for it and the hassle of figuring out which images were duplicates, but this utility seems to fit the bill.
HOSAKA K., A new picture quality evaluation method.
Proc. International Picture Coding Symposium, Tokyo, Japan, 1986, 17-18.
http://linux.maruhn.com/sec/jpegoptim.html
No. You can compress JPEG lossless.
More Noise = Less Compression
I'm a Programmer. That's one level above Software Engineer and one level below Engineer.
First, make a bumpmap of each image. Then, render them onto quads with a light at a 45 degree angle to the surface normal. Run a gaussian blur on each resulting image. Then run a quantize filter, followed by lens flare, solarize, and edge-detect. At this point, the answer will be clear: both images look horrible.
There are 0x40000000 types of people: those who understand 32-bit IEEE 754 floating point, and those who don't.
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.
Except for Lossless JPEG standardized in 1993. But other than that, no there is no lossless jpeg.
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.
Here's a simple but expensive formula:
1. Get the image
2. Compress it severely.
3. Compare the difference between original and the compressed.
The lower the difference, the lower the image quality.
4. Profit!
Or you could just measure the amount of data in the DCT space. Duh.
AI or small utility... You never know with computers ;)
Analogies don't equal equalities, they are merely somewhat analogous.
And a squad of kanagaroos firing RPGs.
Since the mods haven't noticed, and I don't have mod points, let me point out that THIS POST HAS THE ANSWER. A real program that will do what the asker wants. The source is available, but I can't seem to find its license (it includes some of the Independent JPEG Goup's code). Also, doesn't a jpeg's EXIF data or some other tag in the file tell you what quality it was saved at?
The government can't save you.
Try file size on the set of images of interest to you and see if it coincides with your intuition. If it does, you're done.
Lossless JPEG and lossless JPEG2000 are both exactly that - lossless. Not perceptually lossless, which is what people often use to refer to high-quality, lossy JPEG/JPEG2000, or JPEG-LS. Lossless JPEG uses a PCM-like encoder, not DCT, AFAIR. Lossless JPEG and lossless JPEG2000 are, in fact, lossless, at least with regards to image data in supported color spaces. This is in part a result of *not* converting to YCrCb, since that conversion is lossy, of course. Not all Lossless JPEGs are 8bit YCrCb.
Accusoft, for one, has a toolkit for building lossless JPEG applications which supports 16bit RGB and greyscale lossless JPEG modes.
The near-lossless JPEG you're thinking of is JPEG-LS, which is perceptually lossless, and guarantees a maximum error rate that is generally neglible for almost all applications. This format gets better compression ratios than Lossless JPEG, of course.
Neither the lossless or near-lossless JPEG modes are common though, outside of niche apps. Lossless JPEG2000 is, however, since almost all JPEG2000 libraries support it alongside the lossy modes.