Related
I'm looking to do some javascript powered animation via image clipping. Here's an example of what I'm talking about: http://www.def-logic.com/_dhtml/freejack/hero1.gif
I know png uses a kind of prediction in its compression, what would be the best way to lay out an image like the one above so that I get the most out of the compression? I'm especially interested when the images are very similar, more so than the one above, so there is a lot of potential for compression due to redundancy.
For example, is there specific size of tile that would work well?
For example, is there specific size of tile that would work well?
Not really. PNG prediction is strictly local (it uses the 3 neighbours pixels), and the prediction ("filter") strategy can be chosen on a line basis.
That kind of redundancy is not very detectable in PNG compression (not in JPG or practically any other, actually).
If you have the freedom to select the distribution of tiles (few or many per row), you can try vary that, it can have some small influence (to have an image with many short lines instead of few long lines can give the filter better opportunities to select different filters) but, again, I'd bet that the difference will be very small.
As a general rule of thumb when is it appropriate to make a gif interlaced, a png interlaced and a jpeg progressive?
Especially when publishing the image on the web.
JPEG: YES — use progressive scan. It makes files smaller (each pass gets its own Huffman table), and partial rendering looks quite good.
GIF: NO — it's unlikely to make the file smaller, partial rendering is poor, and it's pointless for animGIFs. It's best not to use GIF at all (yes, even for anims).
PNG: NO — it hurts compression (as data from each pass is statistically quite different). If the image is large, use high-quality JPEG or lossy PNG if possible, as these may load quicker than a pixelated preview of a large lossless PNG.
ImageOptim will automatically change progressive/interlaced formats when it makes files smaller.
Disclaimers for nitpickers:
In case of small and medium-sized images the progressive preview of each image is not going to be visible long enough for the user to appreciate it. Some browsers don't even bother rendering anything until the whole file is downloaded, so it's better to focus on saving bandwidth to get the whole page loaded ASAP.
Non-progressive JPEG is a bit more efficient when the files are tiny (small thumbnails), but then the savings are tiny, too.
iOS Safari has a higher maximum allowed image size for baseline JPEG than progressive, but the right solution there is to serve images at sizes reasonable for mobile in the first place.
My general rule of thumb: don't ever use interlacing. Interlaced formats typically occupy more space, have (slightly) more complexity and less support in decoders, and the alleged advantages for the user experience are at least debatable. Some arguments for PNG, and in general.
Some people like interlaced or "progressive" images, which load
gradually. The theory behind these formats is that the user can at
least look at a fuzzy full-size proxy for the image while all the bits
are loading. In practice, the user is forced to look at a fuzzy
full-size proxy for the image while all the bits are loading. Is it
done? Well, it looks kind of fuzzy. Oh wait, the top of the image
seems to be getting a little more detail. Maybe it is done now. It is
still kind of fuzzy, though. Maybe the photographer wasn't using a
tripod. Oh wait, it seems to be clearing up now ...
Interlaced images are slightly less efficient, but show up after shorter delay on the client side when transported over the network. IMHO they should be used when the expected download time for the image is long enough to be perceived by the user (say, above 1 second). The difference in file size is really quite small, so it's better to be too-cautious and use interlacing too much rather than too little.
In common broadband internet as of 2012, I'd just use it for every image > 100kb.
These points must be useful.
Interlacing (more generally, progressive display) is a method of displaying images on a monitor.
When to use it? Your decision should be base on these factors:
•> Non-interlaced images are smaller than interlaced images.
•> Interlaced images cause less flickering than non-interlaced ones
•> Interlaced images are much more easily view-able.
The interlace lets you see the picture before all the data has been transmitted (makes them appear faster and better-looking) and gives you the "feeling" that it is being downloaded faster.
TIP: Interlacing is not recommended for small images but is a must if
the viewer uses a slow connection
This is just a copy from Y answers i thought could help to understand.
Original answer could be find at: https://answers.yahoo.com/question/index?qid=20090211121956AAz7Xz8
Just to throw my twopenneth into the argument: Interlacing was introduced years ago when internet speeds were slow, the idea being that the image would present itself in a gradually more defined manner, still giving an overall look and feel to an image without having to wait for the entire thing to load.
Interlacing, today, is basically unnecessary and should be used based on the overall size of the image being transferred.
Progressive scans on JPEG images images do provide a more refined image while attempting to reduce the overall file size (i.e. is an actual compression mode rather than a streaming method for the bits making up the image).
PNGs use a more complex algorithm than GIF.
There is an interesting related post on webmasters
https://webmasters.stackexchange.com/questions/574/progressive-jpeg-why-do-many-web-sites-avoid-rendering-jpegs-that-way-pros
Untimately it depends on how they are going to be used.
The post suggests that there is limited - genuine - support for progressive images. And sometimes they may cause issues with plugins which don't support the progressive format.
Hope that helps.
For an image-upload tool I want to detect the (subjective) quality of an image automatically, resulting in a rating of the quality.
I have the following idea to realize this heuristically:
Obviously incorporate the resolution into the rating.
Compress it to JPG (75%), decompress it and compare jpg-size vs. decompressed size to gain a ratio. The blurrier the image is, the higher the ratio.
Obviously my approach would use up a lot of cycles and memory if large images are rated, although this would do in my scenario (fat server, not many uploads), and I could always build in a "short circuit" around the more expensive steps if the image exceeds a certain resolution.
Is there something else I can try, or is there a way to do this more efficiently?
Assesing the image (the same goes for sound or video) quality is not an easy task, and there are numerous publications tackling the problem.
Much depends on the nature of the image - different set of criteria is appropriate for artificially created images (i.e. diagrams) or natural images (i.e. photographs). There are subtle effects that have to be taken into consideration - like color masking, luminance masking, contrast perception. For some images a given compression ratio is perfectly adequate, while for other it will result in significant loss of quality.
Here is a free-access publication giving a brief introduction to the subject of image quality evaluation.
The method you mentioned - compressing the image and comparing the result with the original is far from perfect. What will be the metric that you plan to use? MSE? MSE per block? For sure it is not too difficult to implement, but the results will be difficult to interpret (consider images with high-frequency components and without them).
And if you want to delve more into the are of image quality assessment there is also a lot of research done by the machine learning community.
You could try looking in the EXIF tags of the image (using something like exiftool), what you get will vary a lot. On my SLR, for example, you even get which of the focus points were active when the image was taken. There may also be something about compression quality.
The other thing to check is the image histogram - watch out for images biased to the left, which suggests under-exposure or lots of saturated pixels.
For image blur you could look at the high frequency components of the Fourier transform, this is probably accessing parameters relating to the JPG compression anyway.
This is a bit of a tricky area because most "rules" you might be able to implement could arguably be broken for artistic effect.
I'd like to shoot down the "obviously incorporate resolution" idea. Resolution tells you nothing. I can scale an image by a factor of 2 , quadrupling the number of pixels. This adds no information whatsoever, nor does it improve quality.
I am not sure about the "compress to JPG" idea. JPG is a photo-oriented algorithm. Not all images are photos. Besides, a blue sky compresses quite well. Uniformly grey even better. Do you think exact cloud types determine the image quality?
Sharpness is a bad idea, for similar reasons. Depth of Field is not trivially related to image quality. Items photographed against a black background will have a lot of pixels with quite low intensity, intentionally. Again, this does not signal underexposure, so the histogram isn't a good quality indicator by itself either.
But what if the photos are "commercial?" Does the value of the existing technology work if the photos are of every-day objects and purposefully non-artistic?
If I hire hundreds of people to take pictures of park benches I want to quickly know which pictures are of better quality (in-focus, well-lit) and which aren't. I don't want pictures of kittens, people, sunsets, etc.
Or what if the pictures are supposed to be of items for a catalog? No models, just garments. Would image-quality processing help there?
I'm also really interested working out how blurry a photograph is.
What about this:
measure the byte size of the image when compressed as JPEG
downscale the image to 1/4th
upscale it 4x, using some kind of basic interpolation
compress that version using JPEG
compare the sizes of the two compressed images.
If the size did not go down a lot (past some percentage threshold), then downscaling and upscaling did not lose much information, therefore the original image is the same as something that has been zoomed.
Sometimes two image files may be different on a file level, but a human would consider them perceptively identical. Given that, now suppose you have a huge database of images, and you wish to know if a human would think some image X is present in the database or not. If all images had a perceptive hash / fingerprint, then one could hash image X and it would be a simple matter to see if it is in the database or not.
I know there is research around this issue, and some algorithms exist, but is there any tool, like a UNIX command line tool or a library I could use to compute such a hash without implementing some algorithm from scratch?
edit: relevant code from findimagedupes, using ImageMagick
try $image->Sample("160x160!");
try $image->Modulate(saturation=>-100);
try $image->Blur(radius=>3,sigma=>99);
try $image->Normalize();
try $image->Equalize();
try $image->Sample("16x16");
try $image->Threshold();
try $image->Set(magick=>'mono');
($blob) = $image->ImageToBlob();
edit: Warning! ImageMagick $image object seems to contain information about the creation time of an image file that was read in. This means that the blob you get will be different even for the same image, if it was retrieved at a different time. To make sure the fingerprint stays the same, use $image->getImageSignature() as the last step.
findimagedupes is pretty good. You can run "findimagedupes -v fingerprint images" to let it print "perceptive hash", for example.
Cross-correlation or phase correlation will tell you if the images are the same, even with noise, degradation, and horizontal or vertical offsets. Using the FFT-based methods will make it much faster than the algorithm described in the question.
The usual algorithm doesn't work for images that are not the same scale or rotation, though. You could pre-rotate or pre-scale them, but that's really processor intensive. Apparently you can also do the correlation in a log-polar space and it will be invariant to rotation, translation, and scale, but I don't know the details well enough to explain that.
MATLAB example: Registering an Image Using Normalized Cross-Correlation
Wikipedia calls this "phase correlation" and also describes making it scale- and rotation-invariant:
The method can be extended to determine rotation and scaling differences between two images by first converting the images to log-polar coordinates. Due to properties of the Fourier transform, the rotation and scaling parameters can be determined in a manner invariant to translation.
Colour histogram is good for the same image that has been resized, resampled etc.
If you want to match different people's photos of the same landmark it's trickier - look at haar classifiers. Opencv is a great free library for image processing.
I don't know the algorithm behind it, but Microsoft Live Image Search just added this capability. Picasa also has the ability to identify faces in images, and groups faces that look similar. Most of the time, it's the same person.
Some machine learning technology like a support vector machine, neural network, naive Bayes classifier or Bayesian network would be best at this type of problem. I've written one each of the first three to classify handwritten digits, which is essentially image pattern recognition.
resize the image to a 1x1 pixle... if they are exact, there is a small probability they are the same picture...
now resize it to a 2x2 pixle image, if all 4 pixles are exact, there is a larger probability they are exact...
then 3x3, if all 9 pixles are exact... good chance etc.
then 4x4, if all 16 pixles are exact,... better chance.
etc...
doing it this way, you can make efficiency improvments... if the 1x1 pixel grid is off by a lot, why bother checking 2x2 grid? etc.
If you have lots of images, a color histogram could be used to get rough closeness of images before doing a full image comparison of each image against each other one (i.e. O(n^2)).
There is DPEG, "The" Duplicate Media Manager, but its code is not open. It's a very old tool - I remember using it in 2003.
You could use diff to see if they are REALLY different.. I guess it will remove lots of useless comparison. Then, for the algorithm, I would use a probabilistic approach.. what are the chances that they look the same.. I'd based that on the amount of rgb in each pixel. You could also find some other metrics such as luminosity and stuff like that.
I have a very large background image (about 940x940 pixels) and I'm wondering if anyone has tips for compressing a file this large further than Photoshop can handle? The best compression without serious loss of quality from Photoshop is PNG 8 (250 KB); does anyone know of a way to compress an image down further than this (maybe compress a PNG after it's been saved)?
I don't normally deal with optimizing images this large, so I was hoping someone would have some pointers.
It will first depend on what kind of image you are trying to compress. The two basic categories are:
Picture
Illustration
For pictures (such as photographs), a lossy compression format like JPEG will be best, as it will remove details that aren't easily noticed by human visual perception. This will allow very high compression rates for the quality. The downside is that excessive compression will result in very noticeable compression artifacts.
For illustrations that contain large areas of the same color, using a lossless compression format like PNG or GIF will be the best approach. Although not technically correct, you can think of PNG and GIF will compress repetitions the same color very well, similar to run-length encoding (RLE).
Now, as you've mentioned PNG specifically, I'll go into that discussion from my experience of using PNGs.
First, compressing a PNG further is not a viable option, as it's not possible to compress data that has already been compressed. This is true with any data compression; removing the entropy from the source data (basically, repeating patterns which can be represented in more compact ways) leads to the decrease in the amount of space needed to store the information. PNG already employs methods to efficiently compress images in a lossless fashion.
That said, there is at least one possible way to drop the size of a PNG further: by reducing the number of colors stored in the image. By using "indexed colors" (basically embedding a custom palette in the image itself), you may be able to reduce the size of the file. However, if the image has many colors to begin with (such as having color gradients or a photographic image) then you may not be able to reduce the number of colors used in a image without perceptible loss of quality.
Basically it will come down to some trial-and-error to see if the changes to the image will cause any change in image quailty and file size.
The comment by Paul Fisher reminded me that I also probably wouldn't recommend using GIF either. Paul points out that PNG compresses static line art better than GIF for nearly every situation.
I'd also point out that GIF only supports 8-bit images, so if an image has more than 256 colors, you'll have to reduce the colors used.
Also, Kent Fredric's comment about reducing the color depth has, in some situtations, caused a increase in file size. Although this is speculation, it may be possible that dithering is causing the image to become less compressible (as dithering introduces pixels with different color to simulate a certain other color, kind of like mixing pigment of different color paint to end up with another color) by introducing more entropy into the image.
Have a look at http://www.irfanview.com/, is an oldy but a goody.
Have found this is able to do multipass png compression pretty well, and does batch processing way faster than PS.
There is also PNGOUT available here http://advsys.net/ken/utils.htm, which is apparently very good.
Heres a point the other posters may not have noticed that I found out experimentally:
On some installations, the default behaviour is to save a full copy of the images colour profile along with the image.
That is, the device calibration map, usually SRGB or something similar, that tells using agents how to best map the colour to real world-colours instead of device independant ones.
This image profile is however quite large, and can make some of the files you would expect to be very small to be very large, for instance, a 1px by 1px image consuming a massive 25kb. Even a pure BMP format ( uncompressed ) can represent 1 pixel in less.
This profile is generally not needed for the web, so, when saving your photoshop images, make sure to export them without this profile, and you'll notice a marked size improvement.
You can strip this data using another tool such as gimp, but it can be a little time consuming if there are many files.
pngcrush can further compress PNG files without any data loss, it applies different combinations of the encoding and compression options to see which one works best.
If the image is photographic in nature, JPEG will compress it far better than PNG8 for the same loss in quality.
Smush.It claims to go "beyond the limitations of Photoshop". And it's free and web-based.
It depends a lot on the type of image. If it has a lot of solid colors and patterns, then PNG or GIF are probably your best bet. But if it's a photo-realistic image then JPG will be better - and you can crank down the quality of JPG to the point where you get the compression / quality tradeoff you're looking for (Photoshop is very good at showing you a preview of the final image as you adjust the quality).
The "compress a PNG after it's been saved" part looks like a deep misunderstanding to me. You cannot magically compress beyond a certain point without information loss.
First point to consider is whether the resolution has to be this big. Reducing the resolution by 10% in both directions reduces the file size by 19%.
Next, try several different compression algorithms with different grades of compression versus information/quality loss. If the image is sketchy, you might get away with quite rigorous JPEG compression.
I would tile it, Unless you are absolutely sure that you audience has bandwidth.
next is jpeg2k.
To get more out of a JPEG file you can use the 'Modified Quality Setting' of the "Save as Web" dialog.
Create a mask/selection that contains white where you want to keep the most detail, eq around Text. You can use Quick-Mask to draw the mask with a brush. It helps to Feather the selection, this results in a nice white to black transition in the next step.
save this mask/selection as a channel and give the channel a name
Use File->Save as Web
Select JPEG as file format
Next to the Quality box there is a small button with a circle on it. Click that. Select the saved channel in step 2 and play with the quality setting for the white and black part of the channel content.
http://www.jpegmini.com is a new service that creates standard jpgs with an impressively small filesize. I've had good success with it.
For best quality single images, I highly recommend RIOT. You can see the original image, aside from the changed one.
The tool is free and really worth trying out.
JPEG2000 gives compression ratios on photographic quality images that are significantly higher than JPEG (or PNG). Also, JPEG2000 has both "lossy" and "lossless" compression options that can be tuned quite nicely to your individual needs.
I've always had great luck with jpeg. Make sure to configure photoshop to not automatically save thumbnails in jpegs. In my experience I get the greatest bang/buck ratio by using 3 pass progressive compression, though baseline optimized works pretty well. Choose very low quality levels (e.g. 2 or 3) and experiment until you've found a good trade off.
PNG images are already compressed internally, in a manner that doesn't benefit from more compression much (and may actually expand if you try to compress it).
You can:
Reduce the resolution from 940x940 to something smaller like 470x470.
Reduce the color depth
Compress using a lossy compression tool like JPEG
edit: Of course 250KB is large for a web background. You might also want to rethink the graphic design that requires this.
Caesium is the best tool i have ever seen.