Understanding and Interpreting Lux Values

The primary sensor data type for ambient light sensors is illuminance in lux (lumens per square meter). The principles outlined in this topic are based on taking lux values as input and reacting to that data in a program.

Lux readings are directly proportional to the energy per square meter that is absorbed per second. Human perception of light levels is not so straightforward. Human perception of light is complicated because our eyes are constantly adjusting and other biological processes are affecting our perception. However, we can think of this perception from a simplified perspective by creating several ranges of interest with known upper and lower thresholds.

The following example data set represents rough thresholds for common lighting conditions, and the corresponding lighting step. Here, each lighting step represents a change in lighting environment.


This data set is for illustration and may not be completely accurate for all users or situations.


Lighting condition From (lux) To (lux) Mean value (lux) Lighting step
Pitch Black 0 10 5 1
Very Dark 11 50 30 2
Dark Indoors 51 200 125 3
Dim Indoors 201 400 300 4
Normal Indoors 401 1000 700 5
Bright Indoors 1001 5000 3000 6
Dim Outdoors 5001 10,000 7500 7
Cloudy Outdoors 10,001 30,000 20,000 8
Direct Sunlight 30,001 100,000 65,000 9


If we visualize this data by using the mean values from this table, we see that the lux-to-lighting-step relationship is not linear, as show in the following graph.

linear illuminance graph

However, if we view this data by using a logarithmic scale on the x-axis, we can see that a roughly linear relationship emerges.

logarithmic illuminance graph

An Example Transform

Based on the sample data set for ambient light sensors previously provided, you could arrive at the following equation to map lux values to human perception. In this example, the expected values range from 0 lux to 1,000,000 lux.

lux value equation

This equation results in values that vary in a roughly linear fashion between 0.0 and 1.0. This result indicates how human-perceived lighting changed based on the example data set that was shown previously.