Gamma Correction

Attention conservation notice: This note is just a few calculations about gamma correction and some scrappy Python code in case I need it later. You'd be better served reading the Wikipedia page and looking at the cool images.

A collegue, Nick Cheng, likes to adjust grades using gamma correction. This curving process is usually used to brighten up images but it can also help “brighten up” grades. The idea is to replace a grade $x$ with $x^\gamma$ for some value of $\gamma$. If $0 < \gamma \leq 1$ then all the grades go up: the lower grades get increased more and the higher grades get increased less¹.

In 2024, Nick asked a calculus question about this curve: Which grade $x$ gets boosted the most²? Recently, I got wondering about gamma correction again. I wanted to get a sense of the various different values of gammas and the corresponding gamma corrections work. And so, I wrote the scrappy Python program below and calculated some values.

Staring it the values, a few thoughts come to mind:

$\gamma = 0.5$ is too generough. A $10\%$ suddenly tranforms in to $30\%$. Someone with a $25\%$ before the transform would pass $50\%$ afterwards.
$\gamma = 0.8$ is too close to linear. The “brightening up” is very minor.
$\gamma = 0.7$ looks pretty good. Significant boosts, but no miracles, occur. It turns out that $37\%$ maps to a pass, which is still pretty very generous.

The $\gamma=0.5$ Distribution #

Count	Percentage	Gamma
0	0.00	0.00
1	0.10	0.32
2	0.20	0.45
3	0.30	0.55
4	0.40	0.63
5	0.50	0.71
6	0.60	0.77
7	0.70	0.84
8	0.80	0.89
9	0.90	0.95
10	1.00	1.00

The $\gamma=0.7$ Distribution #

Count	Percentage	Gamma
0	0.00	0.00
1	0.10	0.20
2	0.20	0.32
3	0.30	0.43
4	0.40	0.53
5	0.50	0.62
6	0.60	0.70
7	0.70	0.78
8	0.80	0.86
9	0.90	0.93
10	1.00	1.00

The $\gamma=0.8$ Distribution #

Count	Percentage	Gamma
0	0.00	0.00
1	0.10	0.16
2	0.20	0.28
3	0.30	0.38
4	0.40	0.48
5	0.50	0.57
6	0.60	0.66
7	0.70	0.75
8	0.80	0.84
9	0.90	0.92

The Python #

 #!/bin/python3
 
 import math;
 
 N = 10;
 
 gammaList=[0.5, 0.7, 0.8];
 
 for g in gammaList:
     print("# The $\\gamma={}$ Distribution \n".format(g));
     print("| Count | Percentage | Gamma |")
     print("|-------|------------|-------|")
 
     for i in range(0, N+1):
         print("| {} | {:.2f} | {:.2f} |".format(i,i/N,(i/N)**g));
 
     print("\n")

We leave it as exercise to the reader taking Calculus 1 to formalize and check these statements about $x \mapsto x^\gamma$. ↩︎
Another nice exercise. ↩︎

Published: Apr 14, 2026 @ 21:05.
Last Modified: Apr 15, 2026 @ 22:25.

Backlinks

Gamma Correction