blocks|key|799182|text|很简单。如果你要从PNG24转换成PNG8的图像有超过256色，你会失去质量。我错过了什么吗？|type|unstyled|depth|inlineStyleRanges|entityRanges|data|799183|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

Quite simple. If the image you are converting from PNG24 to PNG8 has more thant 256 colors, you gonna loose quality. Do I missed something?

blocks|key|4119231|text|这听起来像是一个全引用的image+quality+assessment问题。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|4119232|解决此问题的最简单方法是尝试计算PNG24和PNG8图像之间的PSNR。这是对两幅图像之间差异的度量。PSNR越高，图像之间的差异越小。使用颜色量化软件后，检查PSNR是否高于某个阈值(您必须根据经验确定)，如果是，则量化是“安全的”。|4119233|PSNR有其缺点，即它并不总是与人类视觉系统的工作方式相对应(例如，它忽略了空间和对比度掩蔽现象)。另一个指标SSIM试图解决这个问题，但计算起来稍微困难一些(尽管这里是一个OpenCV+implementation+)。在我上面描述的阈值方法中，您可以使用SSIM而不是PSNR。|4119234|这是你可能会发现有用的another+thread。|4119235|entityMap|0|LINK|mutability|MUTABLE|url|http://en.wikipedia.org/wiki/Image_quality|1|http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio|2|http://en.wikipedia.org/wiki/Structural_similarity|3|http://mehdi.rabah.free.fr/SSIM/|4|https://stackoverflow.com/questions/4192972/assessing-the-quality-of-an-image-with-respect-to-compression/4194109#4194109^0|C|O|0|0|V|4|1|0|1J|4|2|2M|E|3|0|B|E|4|0^^$0|@$1|2|3|4|5|6|7|Z|8|@]|9|@$A|10|B|11|1|12]]|C|$]]|$1|D|3|E|5|6|7|13|8|@]|9|@$A|14|B|15|1|16]]|C|$]]|$1|F|3|G|5|6|7|17|8|@]|9|@$A|18|B|19|1|1A]|$A|1B|B|1C|1|1D]]|C|$]]|$1|H|3|I|5|6|7|1E|8|@]|9|@$A|1F|B|1G|1|1H]]|C|$]]|$1|J|3|-4|5|6|7|1I|8|@]|9|@]|C|$]]]|K|$L|$5|M|N|O|C|$P|Q]]|R|$5|M|N|O|C|$P|S]]|T|$5|M|N|O|C|$P|U]]|V|$5|M|N|O|C|$P|W]]|X|$5|M|N|O|C|$P|Y]]]]

This sounds like a full-reference <a href="http://en.wikipedia.org/wiki/Image_quality" rel="nofollow noreferrer">image quality assessment</a> problem.

The simplest way to approach this is to try computing the <a href="http://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio" rel="nofollow noreferrer">PSNR</a> between the PNG24 and PNG8 images. This is a measure of the difference between the two images. The higher the PSNR, the less different the images are. After using your color quantization software, check if the PSNR is above some threshold (you'll have to determine that empirically), and if it is, then the quantization was "safe".

PSNR has its down sides, namely the fact that it doesn't always correspond to the way the human visual system works (for example, it neglects the phenomenon of spatial and contrast masking). Another metric, <a href="http://en.wikipedia.org/wiki/Structural_similarity" rel="nofollow noreferrer">SSIM</a>, attempts to take care of that problem, but is slightly more difficult to compute (here is an OpenCV <a href="http://mehdi.rabah.free.fr/SSIM/" rel="nofollow noreferrer">implementation</a>, though). You can use SSIM instead of PSNR in the thresholding approach I described above.

Here's <a href="https://stackoverflow.com/questions/4192972/assessing-the-quality-of-an-image-with-respect-to-compression/4194109#4194109">another thread</a> which you might find useful.

blocks|key|850926|text|对于pngquant的开发，我使用了my+own+SSIM工具，因为基于OpenCV的工具似乎不支持伽马校正或alpha通道。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|850927|当你运行pngquant+-v时，它将输出作为MSE=n引入的误差量(n是均方误差-0是完美质量)。|style|CODE|850928|最新版本有--quality设置，允许您设置最低要求的质量。如果它不能做到这一点，它不会保存文件。|850929|entityMap|0|LINK|mutability|MUTABLE|url|https://github.com/pornel/dssim^0|I|B|0|0|4|B|N|5|Z|1|0|5|9|0^^$0|@$1|2|3|4|5|6|7|R|8|@]|9|@$A|S|B|T|1|U]]|C|$]]|$1|D|3|E|5|6|7|V|8|@$A|W|B|X|F|G]|$A|Y|B|Z|F|G]|$A|10|B|11|F|G]]|9|@]|C|$]]|$1|H|3|I|5|6|7|12|8|@$A|13|B|14|F|G]]|9|@]|C|$]]|$1|J|3|-4|5|6|7|15|8|@]|9|@]|C|$]]]|K|$L|$5|M|N|O|C|$P|Q]]]]

For development of pngquant I use <a href="https://github.com/pornel/dssim" rel="nofollow">my own SSIM</a> tool, since the OpenCV-based one didn't seem to support gamma correction nor alpha channel properly.

When you run <code>pngquant -v</code> it will output amount of error introduced as <code>MSE=n</code> (<code>n</code> is mean square error — 0 is perfect quality).

The latest version has <code>--quality</code> setting which lets you set minimum required quality. If it can't achieve it, it won't save the file.

Hey, i'm using a program called <code>pngquant</code> to convert 24 bit PNGs to 8-bit PNGs. Everything seems to work fine, and I don't notice any loss of quality for icons and other images that don't contain too much colors. Now when I feed it a PNG photo with zillions of colours, it produces a PNG8 where I can see some quality loss.

I'd like to determine that quality loss programmatically. I'd like to know when converting a PNG24 to PNG8 is safe or not. Sort of what webpagetest.org does -- they tell you that this specific image will be smaller in size if converted to PNG8 and will not loose quality.

Any ideas?

Thanks.

How to determine when PNG24 converted to PNG8 is lossless?

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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嘿，我正在使用一个名为pngquant的程序将24位PNG转换为8位PNG。一切似乎都很正常，我没有注意到图标和其他不包含太多颜色的图像有任何质量下降。现在，当我提供给它一张具有无数颜色的PNG照片时，它会生成一个PNG8，其中我可以看到一些质量损失。我想通过编程来确定质量损失。我想知道什么时候将PNG24转换为PNG...

问如何确定何时将PNG24转换为PNG8是无损的？
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问如何确定何时将PNG24转换为PNG8是无损的？EN