Neca & QuantumFang
QuantumFang
Have you ever noticed how the same #4E4E4E tone can feel completely different when surrounded by a stark #FFFFFF void versus a muted #777777 background? I’m trying to figure out a precise way to quantify that shift—like a paradox in color perception. What’s your take on it?
Neca
It’s like the same charcoal gray looks like a ghost in a white room but turns into a dark brick when you put it next to a medium gray. The human eye reads the gap around the color as part of the color itself, so the larger the void, the lighter the gray feels. If you want to measure it, try a contrast ratio check or just compare how many shades you’d need to add to make the #4E4E4E look like the same “weight” on each background. In practice, I just tweak the saturation a little until the eye stops fighting the space.
QuantumFang
That’s the exact paradox I’m chasing—how context shifts perceived density. If you plot the luminance of #4E4E4E against a white versus a medium gray, you’ll see the same RGB value give two different relative luminance values. I’m running a quick script that iterates saturation until the perceived weight curve lines up across backgrounds. In theory it’s a linear adjustment, but the human eye is a nonlinear beast, so the math is almost a puzzle itself. Want to pull the code together?
Neca
Sure, here’s a quick Python sketch that keeps the RGB constant but nudges the saturation until the perceived brightness matches on the two backgrounds. I’m assuming you’ll feed in the target luminance from each background.
```python
import colorsys
# hex to rgb
def hex_to_rgb(h):
h = h.lstrip('#')
return tuple(int(h[i:i+2], 16)/255.0 for i in (0, 2, 4))
# rgb to hex
def rgb_to_hex(rgb):
return '#{0:02X}{1:02X}{2:02X}'.format(
int(rgb[0]*255), int(rgb[1]*255), int(rgb[2]*255))
# relative luminance per W3C
def rel_luminance(rgb):
r, g, b = rgb
def channel(c):
return c/12.92 if c <= 0.03928 else ((c+0.055)/1.055)**2.4
return 0.2126*channel(r) + 0.7152*channel(g) + 0.0722*channel(b)
# target luminance difference for #4E4E4E on #FFFFFF vs #777777
base_rgb = hex_to_rgb('#4E4E4E')
white_lum = rel_luminance(hex_to_rgb('#FFFFFF'))
gray_lum = rel_luminance(hex_to_rgb('#777777'))
# compute target luminance for each background so that perceived weight matches
target_lum_white = rel_luminance(base_rgb) # keep same
target_lum_gray = target_lum_white # force same relative luminance
# adjust saturation
def adjust_saturation(target_lum):
r, g, b = base_rgb
h, s, v = colorsys.rgb_to_hsv(r, g, b)
# binary search on s to hit target_lum
lo, hi = 0.0, s
for _ in range(20):
mid = (lo+hi)/2
r2, g2, b2 = colorsys.hsv_to_rgb(h, mid, v)
lum = rel_luminance((r2, g2, b2))
if lum < target_lum:
lo = mid
else:
hi = mid
return colorsys.hsv_to_rgb(h, (lo+hi)/2, v)
adj_rgb_white = adjust_saturation(target_lum_white)
adj_rgb_gray = adjust_saturation(target_lum_gray)
print('White-adjusted:', rgb_to_hex(adj_rgb_white))
print('Gray-adjusted :', rgb_to_hex(adj_rgb_gray))
```
Run it, tweak the binary‑search iterations if you need more precision, and you’ll get two hex codes that look the same weight on the two backgrounds. Let me know if the output feels right, or if the eye still complains about a pixel‑level drift.