1. Context-aware Exposure Auto-correction

2. Global exposure auto-correction
over-exposed
under-exposed
low-contrast
input
automatic histogram stretching

3. Global exposure auto-correction
Detection: valid histogram range < threshold
Method: stretch histogram, adjust gamma curve
Test Images
# Global Correction
Percentage
1370 63
4.6%
#: Globally over-exposed, under-exposed & low-contrast images
Test Images include party, family, vacation, landscape, street view, pets

4. Local exposure auto-correction
High dynamic range scene
input
Auto adjustment
[WLPG]
Local shadow /
Highlight [ours]

5. Local exposure auto-correction
Back-lighting object
input
Auto adjustment
[WLPG]
Local shadow /
Highlight [ours]

6. High dynamic range scene detection
segment
sky region
scene region
input
extract features
sky detection , ,
local contrast
in scene region
sky histogram
scene histogram
confidence map of sky
classifier

7. Samples of high dynamic range scene
True:
False:

8. High dynamic range scene
Test Images
detected sky image
Percentage
1370
316
23%
True HDR scene
# Percentage
False HDR Scene
190
13.87%
126
#: True HDR scene images / Test Images

9. Back-lighting object detection
The most attractive backlit object is human!
extract
features
face
detection
Histogram, local contrast
in face/body region
input
classifier
Histogram of image
body
detection
input

10. Samples of back-lighting object
True:
False:

11. Back-lighting object Test Images detected human image Percentage 1370
595
43.4%
True backlit human
# Percentage
False backlit human
102
7.44%
493
#: True backlit human images / Test Images

12. ( hdr scene 13.87% + backlit human 7.44% )
Summary
Global incorrect exposure v.s. local incorrect exposure
The “detected Human + Sky images” account for almost 66.5% of the whole test images
Global
Local
4.6%
21.31%
( hdr scene 13.87% + backlit human 7.44% )