<p>I am visualizing a dataset. My technique is to encode the data into a rgb image with the channel range [0, 255]. The data clusters in a narrow range relative to [0, 255], however, so I'm not getting enough spread in the data to get a legible visualization when I use the standard normalization:</p>

<pre><code>255 * (dataValue - dataMin) / (dataMax - dataMin)
</code></pre>

<p>What non-linear normalization techniques could I use? I know the mean and standard deviation.</p>

CC BY-SA 4.0
2020-08-10T17:11:12.550Z
Histogram equalization makes a good image, but does't it distort the data values? This is a geospatial visualization of environmental data and the color is keyed to a colormap which is in turned keyed to numerical values.
interwebjill
CC BY-SA 4.0
2020-05-21T21:59:48.023Z
Are you ok with throwing away outliers by mapping them outside the [0, 255] range? If so, you could normalize to place the mean at 128 and linearly map 2 or 3 standard deviations around it to [0, 255].
Nathan Reed
CC BY-SA 4.0
2020-05-22T03:58:24.093Z
This is ocean currents data, so the occasional outliers are the interesting bits, but they are few and far between. Relative to these, the ocean appears calm, unless you can expand those more common data points to reveal the underlying currents.
interwebjill
CC BY-SA 4.0
2020-05-23T09:03:25.893Z
Try histogram equalization.
lightxbulb
<normal-mapping><raster-image>
Normalization technique to increase spread of data in rgb space
interwebjill
# Normalization technique to increase spread of data in rgb space

(Asked by: username_0 on 2020-05-21)

I am visualizing a dataset. My technique is to encode the data into a rgb image with the channel range [0, 255]. The data clusters in a narrow range relative to [0, 255], however, so I'm not getting enough spread in the data to get a legible visualization when I use the standard normalization:

```
255 * (dataValue - dataMin) / (dataMax - dataMin)

```

What non-linear normalization techniques could I use? I know the mean and standard deviation.

**Tags:** normal-mapping, raster-image

> **Comments:**
> **username_1**: Are you ok with throwing away outliers by mapping them outside the [0, 255] range? If so, you could normalize to place the mean at 128 and linearly map 2 or 3 standard deviations around it to [0, 255].  
> **username_0**: This is ocean currents data, so the occasional outliers are the interesting bits, but they are few and far between. Relative to these, the ocean appears calm, unless you can expand those more common data points to reveal the underlying currents.  
> **username_2**: Try histogram equalization.  
> **username_0**: Histogram equalization makes a good image, but does't it distort the data values? This is a geospatial visualization of environmental data and the color is keyed to a colormap which is in turned keyed to numerical values.  

---

## 0 answers

