Lossy Image Compression Metrics - image-processing

I'm wondering about what types of metrics I can use for comparing different (lossy) image compression methods (i.e., things other than compression ratio). For example, comparing JPEG, JPEG 2000, and JPEG XR on a set of different images.
Ideally I'd like to do this in a python notebook, but I'm open to any suggestions.
Thank you!

I think this boils down to comparing the lossy images to their original, i.e. measuring the loss of image quality.
Measure the difference between the lossy image and its origin, do the same for other lossy images and compare the results.
How this can be done has been asked before and I have just added an answer mentioning some approaches.

Related

Why image compression doesnt use overlapped data?

For example in audio codecs like Opus, MDCT is used with 50% percent overlap to avoid ringing artifacts. Why a similar approach is not used in image codecs. e.g., JPEG uses non-overlapping 8x8 blocks ?
Later lossy image codecs like JPEG2000 do use overlapped transforms, but these techniques just weren't around when JPEG was being defined. The wavelet transform that JPEG2000 is based on hadn't been invented yet, and time-domain anti-aliasing techniques like MDCT were extremely new.
For the MDCT in particular, as far as I know it is not used for image compression at all, even Today. I would guess that's because its basis vectors are asymmetric, which makes it intuitively difficult to choose for imaging applications.

What is the best format for using SIFT?

Is there any difference in terms of precision and speed in using SIFT with JPEG, PNG or PGM images? Obviously supposing the same image size.
This question does not make sense.
SIFT is an algorithm, it operates on raw pixel data in memory, not on files.
You would need some framework that loads image files into some data structure in memroy.
SIFT itself doesn't care if you load a jpeg or a pgm. It will never know where the pixels came from.
Having the same size (to 1 bit) for the same image in three different formats would be pretty impossible in my opinion. As PGM is uncompressed this would also mean that JPEG and PNG would have to be uncompressed to have roughly the same size. If you have no compression you have no losses. So there would be no difference in SIFT performance.

Lossy png compression

What sort of library would be recommended for lossy png compression while retaining alpha layer.
I use kraken.io a lot but would like to work it more into a workflow.
I know a lot about lossless compression but I'm at a loss with how they do lossy.
If you literally want a library, then libimagequant (I think that's what kraken.io uses).
There's also mediancut posterizer that achieves lossy compression by preprocessing. It's much more effective than posterization in Photoshop or ImageMagick.
Description of lossy PNG methods on ImageAlpha website.
PNG is lossless by design, there's no lossy PNG. All you can do is some lossy preprocessing before saving it as PNG, as converting a true colour image to a palette one, or by reducing the number of colors by posterize - all that will probably reduce the size of the image, but that's not part of PNG.

Image processing with lossy compression

If we compare image procesing of the losslessly compressed images with the image processing of the lossy compressed images, does the latter provide the results comparable to the former one.
I am asking this question because the images prodiced by lossless compression are ok for human eye but they vary at minute details which may effect the processing of images by the computer. But I can't tell how much.
I don't see much of a question here, but you are right. It is especially visible if processing a JPG image with a medium compression ratio -- the 8x8 squares of which JPG's are built of tend to get more visible after filtering.
This is comparable to the rising of computational error when operating on computer-based floating point numbers.
Your best bet for image processing is using lossless formats for image processing -- PNG's are a good choice, cause they both provide lossless compression, as well as a decent support for bitdepths, transparency and are browser-compatible.
Another format, more often used in the professional world are TIFF's (Targa).
However, note that if your source image is already in a loss-based format, converting it to a lossless one will only prevent adding additional artifact's, not spreading and enhancing the old one. You can however reduce the extent of error by converting it to a lossless format and running it through a small seed gaussian blur.
Perhaps you are looking for the Perceptual Image Diff utility?

Is it possible to tell the quality level of a JPEG?

This is really a two part question, since I don't fully understand how these things work just yet:
My situation: I'm writing a web app which lets the user upload an image. My app then resizes to something displayable (eg: 640x480-ish) and saves the file for use later.
My questions:
Given an arbitrary JPEG file, is it possible to tell what the quality level is, so that I can use that same quality when saving the resized image?
Does this even matter?? Should I be saving all the images at a decent level (eg: 75-80), regardless of the original quality?
I'm not so sure about this because, as I figure it: (let's take an extreme example), if someone had a 5 megapixel image saved at quality 0, it would be blocky as anything. Reducing the image size to 640x480, the blockiness would be smoothed out and barely less noticeable... until I saved it with quality 0 again...
On the other end of the spectrum, if there was an image which was 800x600 with q=0, resizing to 640x480 isn't going to change the fact that it looks like utter crap, so saving with q=80 would be redundant.
Am I even close?
I'm using GD2 library on PHP if that is of any use
You can view compress level using the identify tool in ImageMagick. Download and installation instructions can be found at the official website.
After you install it, run the following command from the command line:
identify -format '%Q' yourimage.jpg
This will return a value from 0 (low quality, small filesize) to 100 (high quality, large filesize).
Information source
JPEG is a lossy format. Every time you save a JPEG same image, regardless of quality level, you will reduce the actual image quality. Therefore even if you did obtain a quality level from the file, you could not maintain that same quality when you save a JPEG again (even at quality=100).
You should save your JPEG at as high a quality as you can afford in terms of file size. Or use a loss-less format such as PNG.
Low quality JPEG files do not simply become more blocky. Instead colour depth is reduced and the detail of sections of the image are removed. You can't rely on lower quality images being blocky and looking ok at smaller sizes.
According to the JFIF spec. the quality number (0-100) is not stored in the image header, although the horizontal and vertical pixel density is stored.
For future visitors, checking the quality of a given jpeg, you could just use imagemagick tooling:
$> identify -format '%Q' filename.jpg
92%
Jpeg compression algorithm has some parameters which influence on the quality of the result image.
One of such parameters are quantization tables which defines how many bits will be used on each coefficient. Different programs use different quatization tables.
Some programs allow user to set quality level 0-100. But there is no common defenition of this number. The image made with Photoshop with 60% quality takes 46 KB, while the image made with GIMP takes only 26 KB.
Quantization tables are also different.
There are other parameters such subsampling, dct method and etc.
So you can't describe all of them by single quality level number and you can't compare quality of jpeg images by single number. But you can create such number like photoshop or gimp which will describe compromiss between size on quality.
More information:
http://patrakov.blogspot.com/2008/12/jpeg-quality-is-meaningless-number.html
Common practice is that you resize the image to appropriate size and apply jpeg after that. In this case huge and middle images will have the same size and quality.
Here is a formula I've found to work well:
jpg100size (the size it should not exceed in bytes for 98-100% quality) = width*height/1.7
jpgxsize = jpg100size*x (x = percent, e.g. 0.65)
so, you could use these to find out statistically what quality your jpg was last saved at. if you want to get it down to let's say 65% quality and if you want to avoid resampling, you should compare the size initially to make sure it's not already too low, and only then reduce the quality
As there are already two answers using identify, here's one that also outputs the file name (for scanning multiple files at once):
If you wish to have a simple output of filename: quality for use on multiple images, you can use
identify -format '%f: %Q' *
to show the filename + compression of all files within the current directory.
So, there are basically two cases you care about:
If an incoming image has quality set too high, it may take up an inappropriate amount of space. Therefore, you might want, for example, to reduce incoming q=99 to q=85.
If an incoming image has quality set too low, it might be a waste of space to raise it's quality. Except that an image that's had a large amount of data discarded won't magically take up more space when the quality is raised -- blocky images will compress very nicely even at high quality settings. So, in my opinion it's perfectly OK to raise incoming q=1 to q=85.
From this I would think simply forcing a decent quality setting is a perfectly acceptable thing to do.
Every new save of the file will further decrease overall quality, by using higher quality values you will preserve more of image. Regardless of what original image quality was.
If you resave a JPEG using the same software that created it originally, using the same settings, you'll find that the damage is minimized - the algorithm will tend to throw out the same information it threw out the first time. I don't think there's any way to know what level was selected just by looking at the file; even if you could, different software almost guarantees different parameters and rounding, making a match almost impossible.
This may be a silly question, but why would you be concerned about micromanaging the quality of the document? I believe if you use ImageMagick to do the conversion, it will manage the quality of the JPEG for you for best effect. http://www.php.net/manual/en/intro.imagick.php
Here are some ways to achieve your (1) and get it right.
There are ways to do this by fitting to the quantization tables. Sherloq - for example - does this:
https://github.com/GuidoBartoli/sherloq
The relevant (python) code is at https://github.com/GuidoBartoli/sherloq/blob/master/gui/quality.py
There is another algorithm written up in https://arxiv.org/abs/1802.00992 - you might consider contacting the author for any code etc.
You can also simulate file_size(image_dimensions,quality_level) and then invert that function/lookup table to get quality_level(image_dimensions,file_size). Hey presto!
Finally, you can adopt a brute-force https://en.wikipedia.org/wiki/Error_level_analysis approach by calculating the difference between the original image and recompressed versions each saved at a different quality level. The quality level of the original is roughly the one for which the difference is minimized. Seems to work reasonably well (but is linear in the for-loop..).
Most often the quality factor used seems to be 75 or 95 which might help you to get to the result faster. Probably no-one would save a JPEG at 100. Probably no-one would usefully save it at < 60 either.
I can add other links for this as they become available - please put them in the comments.
If you trust Irfanview estimation of JPEG compression level you can extract that information from the info text file created by the following Windows line command (your path to i_view32.exe might be different):
"C:\Program Files (x86)\IrfanView\i_view32.exe" <image-file> /info=txtfile
Jpg compression level is recorded in the IPTC data of an image.
Use exiftool (it's free) to get the exif data of an image then do a search on the returned string for "Photoshop Quality". Or at least put the data returned into a text document and check to see what's recorded. It may vary depending on the software used to save the image.
"Writer Name : Adobe Photoshop
Reader Name : Adobe Photoshop CS6
Photoshop Quality : 7"

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