计算人类如何感知不同颜色之间的相似性 [英] Calculate how humans perceive similarity between different colours
问题描述
我在一个网站上，用户可以使用（在许多其他事物中）rgb 0-255范围内的任何颜色描述一个物理对象。
在幕后，其中一个进程会比较对象的两个用户描述，并对它们进行评分对于相似性。
我想要做的是获得一个分数，在人类感知方面，这两种颜色是如何相似的。基本上，算法需要确定是否2个人选择2种不同的颜色可以描述相同的对象。因此，浅红色 - >红色应该是100％，大多数灰色阴影将是100％彼此等，但红色 - 绿色绝对不是匹配。
为了看看算法如何工作，我绘制灰度和每种色调的3种强度与集合中的每个其他颜色，并指示与黑色，视觉上相同（100％）与白色和黑色的无匹配（0％）灰度表示中间值。
我的第一个（非常简单的方法）是简单地将RGB值视为颜色立方体中的坐标，并计算距离
这抛出了一些关于Black-> 50％灰色的问题是比较大的距离比如说Black-> 50％蓝色。已经运行了数百个比较并要求反馈，这似乎不符合人类的感觉（如下所示）
方法2将RGB值转换为有一个专门的页面。在列出的那些中，DeltaE 2000可能与人类感知具有最佳相关性，并且计算也非常复杂。 Delta CMC对于类似代码的一半（虽然计算还不是很简单）来说几乎是。
I'm working on a site where users can describe a physical object using (amongst many other things) any color in the rgb 0-255 range. We offer some simplified palettes for easy clicking but a full color wheel is a requirement.
Behind the scenes, one of the processes compares two user descriptions of the object and scores them for similarity.
What I'm trying to do is get a score for how similar the 2 colors are in terms of human perception . Basically, the algorithm needs to determine if a 2 humans picking 2 different colors could be describing the same object. Thus Light Red->Red should be 100%, Most of the shades of grey will be 100% to each other, etc but red-> green is definitely not a match.
To get a decent look at how the algorithms were working, I plotted grayscale and 3 intensities of each hue against every other color in the set and indicated no match (0%) with black, visually identical (100%) with white and grayscale to indicate the intermediate values.
My first (very simplistic approach) was to simply treat the RGB values as co-ordinates in the colour cube and work out the distance (magnitude of the vector) between them.
This threw out a number of problems with regards to Black->50% Grey being a larger distance than (say) Black->50% Blue. having run hundreds of comparisons and asked for feedback, this doesn't seem to match human perception (shown below)
Method 2 converted the RGB values into HSV. I then generated a score based 80% on hue with the other 20% on Sat/Lum. This seems to be the best method so far but still throws some odd matches
Method 3 was an attempt at a hybrid - HSL Values were calculated but the final score was based upon the distance between the 2 colors in the HSL color cylinder space (as in 3D polar co-ordinates).
I feel like I must be re-inventing the wheel - surely this has been done before? I can't find any decent examples on Google and as you can see my approach leaves something to be desired.
So, my question is:
Is there a standard way to do this? If so, how? If not, can anyone suggest a way to improve my approach? I can provide code snippets if required but be warned it's currently messy as hell due to 3 days of tweaking.
Solution (Delta E 2000):
Using the suggestions provided below, I've implemented a Delta E 2000 comparer. I've had to tweak the weighting values to be quite large - I'm not looking for colors which are imperceptibly different but which are not hugely different. In case anyone's interested, the resulting plot is below...
There are a half dozen or so possibilities. EasyRGB has a page devoted to them. Of those listed, DeltaE 2000 probably has the best correlation with human perception -- and is also extremely complex to compute. Delta CMC is almost as good for something like half the code (though the computation still isn't entirely trivial).
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