Not sure what the objective is, but using the "before" example 475kb lena pic, a subsequent jpg at 61k looks very close to the original, and much better than the 61k blurred png.
There is a reason why jpg has survived the test of time. It delivers a good balance of quality, performance, and is well supported. Challengers like JPEG 2000 have not gained much traction because jpg gets the job done.
http://en.wikipedia.org/wiki/JPEG_2000
This is a godsend. Thank you to the developer, I'm already using it. It's cut transparent png-24s by 60% with no perceivable difference. Graphic designers rejoice!
The author's tool is excellent and achieves impressive ratios.
But once you open the "I can accept information loss" door, it might be worthwhile to experiment with other image manipulations also. For example, consider dropping color depth. Some images survive that process well.
Here's a 1-minute experiment... take the lenna.png image from the article... open in GIMP, posterize to 27 levels (or whatever you think is acceptable), export back to PNG... 43% savings.
That's the second option in the article (pngquant2).
Last time I checked GIMP's palette generation wasn't very good - it supported only binary transparency and truncated bits unnecessarily.
I've wanted to replace GIMP's old algorithm, but haven't even managed to compile all the prerequisites for the monstrous codebase :(
One more thing I do in pngquant2 and haven't seen it done anywhere else is dithering only areas that need it, rather than dithering entire image. This minimizes noise added and makes files look and compress better.
Also worthwhile to explore other image formats, of course.
For photos (like lenna.png), jpg is nearly always a better choice if you're ok with lossless, even better than png with the tricks mentioned in the article.
Of course there are some situations where png simply has features you want, like an alpha-channel, but some people even work around that (eg. in some videogames) by encoding the image part as jpg and a separate 8-bit alpha map in a lossless format to go with it and these are merged back together at load time. Not really suitable for the web, though (possible, but wildly inefficient compared to just letting the browser do the compositing).
I've never understood how a format can be "lossy" or "lossless" in the first place. It seems to me that the format specifies how the image data should be rendered on a display device and any "loss" that occurs us always a result of the encoding process. For that matter, the concept of loss only makes sense if you start with a pixmap, and that is not always the case.
Technically you can have a format which is literally defined in such a way that not all inputs can be perfectly represented. On example is mobile phone audio, it is defined to operate at a particular bit rate, which is less than that required to represent all possible speech signals.
However, you are right that 'lossy' is usually used to describe its intended use rather than its actual formal limit. For example, you can (probably!) non-lossily encode an image as a jpeg, but it won't be as good a compression ratio as you would get from a format which was specifically designed for use with lossless compression. It might even cause the file to get bigger.
By definition, a CODEC starts with some series of bits (source) that encodes information into a new series of bits (encoding). A CODEC is said to be "lossy" when the results of the decoded encoding does not match the source, ie. information encoded in the source stream gets dropped.
Various techniques are used to accomplish this. One side of the scale is to simply degrade the quality of the original. The other is to use visual/acoustic changes humans have difficulty in recognizing. For instance, the human hear has a minimum distance between frequency and loudness of two tones. The softer tone is dropped in MP3 depending on "compression" level; thus, from a source fidelity standpoint, the quality is "lossy" since the encoded form no longer has the original's content. From a human ear quality standpoint, the quality is barely noticeable to most people.
Here's the issue I think gweinberg has: Imagine you encode an image to JPEG, with the "quality slider" cranked all the way to 11 (or whatever the maximum is). Now, the DCT blocks are tiny and the compression is super inefficient. But it exactly reproduces the input image, and you may say it's lossless. The same image encoded with PNG (deflate) might be way smaller, but that still does not change the fact that JPEG losslessly encodes this image.
Consider an extremely simple codec that uses only (lossless) run-length encoding [1]. You have an image of a blue sky with a few white clouds. One of the rows might have pixels Blue-Blue-Blue-Blue-White-White. This would be encoded as 4Blue-2White. The amount of information is the same, but you've removed redundancy (this is more or less how fax machines work).
A lossy codec would take advantage of limits in human perception and encode LightBlue-MediumBlue-DarkBlue as 2LightBlue-DarkBlue. On a large enough image, you wouldn't notice the difference.
No, a lossy algorithm can be idempotent, and in fact any optimal encoder is necessarily idempotent (because there is no need to further degrade an already compressed image). While it's true that most encoders are not actually optimal and often add noise when re-encoding, it's not a theoretical necessity.
The correct mathematical term for "lossy" is "non-surjective": at a given encoding setting, not all inputs can be represented.
I haven't used pngquant in years (the last time I used it was for supporting PNG transparency in IE6), but I happened to use the excellent linked tool ImageAlpha on an HTML5/Flash game we are in the middle of shipping and managed to cut 1.2MB out of 7+MB in assets from it. That's pretty impressive.
The interesting thing is that the blur filter gave zero benefit, but the "median cut" pngquant to palletize the images gave some pretty impressive gains.
This is cool code but can someone explain why we'd need this? Do we not have a common lossy format that supports transparency? My primary concern is that people have an expectation of what PNG means and this completely subverts that.
We actually don't, WebP can do this, but it's by no means common yet. This is actually a reasonable use of PNG still, it's optimizing the contents for the lossless algorithm. You might be able to take the difference/error and put it in another image and layer them to make it give back the detail.
PNG isn't being subverted. The lossage happens before PNG is applied.
What you are getting is a lossless copy of an image that has been cleverly preprocessed to be easier for the PNG algorithm to compress while not losing noticeable visual quality. The result is good compression and transparency support (which JPEG doesn't have).
Yeah I only asked because I agree with the above poster that we can't consider WebP common yet but was unsure if I was forgetting something. I can see the utility in this then, at least as a stopgap.
I've researched lossy GIF too: http://pornel.net/lossygif but even lossy version wasn't better than PNG.
LZW is just a very poor compression. Even best case is taking ridiculous amount of bits (you can only add 1 byte to previously used pattern, so it's sequence of symbols for 1+2+3+5+6+etc. pixels resetting every 4k iterations), so there isn't even room for improvement.
Yes, I'd like to add it to ImageOptim. However, I don't want anybody to accidentally ruin their source images, so I'm looking for an elegant UI for lossy optimizations. Feedback welcome:
On my browser (default browser on Galaxy Nexus) the pallete example and posterization example (tree and mask pictures) have weird scan lines. I guess the palette support is not perfect in every browser.
I can't seem to find a license file in the GitHub repo. Since no permissions are granted by default, this is effectively not open source. I've opened a GitHub issue requesting the addition of a LICENSE file: https://github.com/pornel/ImageAlpha/issues/9.
This is very cool. I've wondered for a long time if sophisticated, predictor-aware lossy compression could be done with PNG. I'd love to read a paper on how this works.
I'm not sure how far you'd get; since PNG's predictor selection is IIRC only done on a scanline basis you don't have as many opportunities to really leverage prediction. Lossy image compression typically operates using a combination of planar modifications (like subsampling) and block-level modifications.
On the flipside, part of why PNG does so well on lossless compression is due to the scanline-oriented predictor selection - that allows it to beat regular GZIP by identifying ways to losslessly compress 2D image patterns that aren't necessarily obvious in the 1D stream of bytes being sent to the compressor.
I think the goal should be a lossless format for audio,video, and images so that the loss that keeps occurring over time as these objects are recompressed with lossy formats doesn't occur. PNG was that for images but now someone is suggesting a lossy format, of course it looks fine the first time but then when the next compression comes out and people move to that you incur another loss.
He's finessing png's lossless compression by removing detail from the source image. So it's lossy compression using a lossless file format (just like you can reduce the palette of an image and the dithering algorithm to reduce gif file sizes).
Blur is a low-pass filter, filtering out higher frequencies in the image. It's the exact same steps as you use with compression. The difference is that you're not using additional transforms and their coefficients to mitigate artifacts and/or slightly improve quality.
They are changing the encoder in such a way that the resulting PNG does not decode exactly, but instead approximates the original image. This turns it into a lossy process. It doesn't really matter if this process happens before or after the PNG encoding step (in fact, from how they describe it, it sounds like it it is integrated into the PNG encoding process, not simply a pre-processor); the combined process of any filtering and PNG encoding is lossy, as in it does not preserve all information necessary to produce a bit-identical output.
Interesting but misleading. The author applied a blur filter which allowed the non-lossy PNG format to compress better. That's not lossy PNG.
The information is lost in the blur (or other pre-process) before PNG gets ahold of it.
As I understood it, I think that's opposite of what he's saying. He's saying blur helps restore info.
From the description, I understood the information is lost by deliberately omitting certain pixels that PNG rendering will try to restore from adjacent pixel data, and that he did a diagonal blur to influence the adjacent pixels to contribute better data to the missing ones. Blurring spread info into diagonally adjacent pixels improving results of using them to reconstruct missing ones.
// source is invoking three png utils, so didn't dig into what it's really doing, just saying I think the explanation is that reconstructing pixels saves space and reconstruction is improved by letting diagonal neighbors contain more info on reconstructed pixel through blur.
Incorrect. PNG does not do reconstruction, just delta compression. By applying a diagonal blur, he is removing entropy in a way that the lossless compressor is likely to take advantage of.
I didn't say PNG did anything, so I'm neither correct nor incorrect about what PNG does (though on second read compressing is a better word than omitting, though either way the "guessed" pixel is where the information goes missing). I'm disputing parent's claim author said savings was from blur. He didn't. He said:
> PNG has an ability to “guess” pixels based on their top and left neighbors and successful guesses compress to almost nothing. Usually only few pixels match a guess, but latest ImageAlpha's “Blurizer” option manipulates image data to match the guesses, making compression much much more effective.
The only time I use PNG is if I'm aiming for pixel-crisp, anything else is JPEG where fitting. I LIKE my PNGs to be pixel-crisp, and the only way to substitute that I could think of is put PNGs in /lossless and /lossy folders individually.
Maybe a quick JS speed test and if possible switch all your lossy to lossless? Would look weird on load without a splash screen though.
I was able to shrink an iOS app from 9mb to 4.5mb by using ImageAlpha + ImageOptim. There's a case study about TweetBot how they did the same (http://imageoptim.com/tweetbot.html).
The situation has probably worsened. Opera lost out-of-the-box APNG support with the switch to Webkit. Firefox has lost market share to Chrome on the desktop, and were never big in the (still growing) mobile market.
There is a simple Chrome extension that adds APNG support, but anything that is not installed by default is unlikely to be used by most users. 90% of Chrome users don't even install an ad blocker!
"Batch processing
Available from command line. ImageAlpha is based on pngquant. You'll find compiled pngquant executable in ImageAlpha.app/Contents/Resources directory."
