计算人类如何感知不同的颜色之间的相似性 [英] Calculate how humans perceive similarity between different colours

问题描述

我工作的一个网站，用户可以使用描述物理对象（以及许多其他的东西）的任何颜色的RGB 0-255范围内。我们提供方便点击，但一个完整的色轮是必需的。

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.

在幕后，其中一个进程比较对象的两个用户的描述和分数它们的相似性。

Behind the scenes, one of the processes compares two user descriptions of the object and scores them for similarity.

我想要做的就是一个比分为2种颜色的相似程度的在人类感知方面的。基本上，该算法需要确定一个2人类拾取2不同的颜色可描述相同的对象。因此光红 - >红应该是100％，大多数灰的色调将是100％给对方，等，但红 - >绿是绝对不匹配。

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.

要得到一个体面看看如何算法进行了工作，我暗算了集中的每个其他颜色的每个色调的灰度和3强度，并表示不匹配（0％），黑色，视觉上相同（100％）与白和灰度来表示中间值。

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.

我的第一个（非常简单的方法）是简单地将RGB值作为坐标的颜色立方体以及它们之间制定的距离（矢量幅度）。

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.

这抛出了许多问题与问候黑色 - > 50％灰比（比如说）黑色 - > 50％蓝较大的距离。已经运行数百个比较，并要求反馈，这似乎并不符合人类的感知（如下图所示）

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)

方法2转换的RGB值到 HSV 。然后我生成一个基于分数80％的色调与饱和/绥另外的20％。这似乎是最好的方法，到目前为止，但仍引发一些奇怪的比赛

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

方法3是在一个混合的尝试 - 分别计算HSL值但最终得分是基于所述2颜色的HSL颜色筒空间之间的距离（如在三维极坐标）

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).

我觉得我必须重新发明轮子 - 当然这之前已经做了什么？我找不到对谷歌的任何像样的例子，你可以看到我的做法令人不满意之处。

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.

所以，我的问题是：

有没有一种标准的方式来做到这一点？如果是这样，怎么样？如果没有，任何人都可以提出一个方法来提高我的做法？如果需要，我可以提供code段，但被警告它目前的地狱，由于调整了3天乱了。

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.

解决方案（的ΔE2000）： 使用下面提供的建议，我已经实现了一个的ΔE2000 比较器。我不得不调整的加权值是相当大的 - 我不是在寻找的颜色这是潜移默化的不同，但它们是不是巨大的差别。如果任何人的兴趣，由此产生的积低于...

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...

推荐答案

有半打左右的可能性。 EasyRGB 有专门为他们准备的页面。那些上市，2000的DeltaE可能与人类感知相关性最好 - 也是极其复杂的计算。三角洲CMC是的几乎的，好，像半个code（虽然计算仍然是不完全微不足道的）。

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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