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Table 1: Rank of the subjective visual performance.
C = Contrast, D = Details, N = Naturalness
Rank C D C/N D/N C/D/N
1 d k d f f
2 j f f b d
3 c g h e h
4 h b e h e
5 i e a d b
6 a h c g g
7 e d i a a
8 f a b i i
9 g i g c c
10 b c j k k
11 k j k j j
f is clearly worse but still within the upper range con-
cerning the original 12bit RMS values. Therefore, we
can recommend algorithm d as a tone mapper in in-
dustrial image processing applications where fast ac-
quisition times and high-contrast images are needed.
5 CONCLUSIONS
We have presented a unified framework and modi-
fied tone mapping operators for the purpose of single-
shot HDR imaging. The goal was to enhance the vi-
sually perceived contrast of tone mapped LDR im-
ages, thereby preserving most textural detail of the
original HDR images in both bright and shadowed
regions. The qualitative evaluation shows that this
was successfully achieved with our newly introduced
dynamic scene key approach. It has been shown
that the implementation of tonal normalization after
tonal compression should be taken care of because
the clamping strategy for out of range intensities has
a measurable effect on the subjective perception of
the mapping result. Finally, we introduced a region-
based noise reduction and selective sharpening ap-
proach that can be added to the general tone mapping
framework in order to enhance the performance of al-
ready existing mapping operators. In our evaluation
section we have outlined general criteria for subjec-
tive evaluation of tone mapping results.
REFERENCES
Bodrogi, P. and Khanh, T. Q. (2012). Illumination, Color
and Imaging: Evaluation and Optimization of Visual
Displays. Wiley Series in Display Technology. Wiley-
VCH Verlag GmbH & Co. KGaA.
DiCarlo, J. M. and Wandell, B. A. (2000). Rendering high
dynamic range images. Sensors and Camera Systems
for Scientific, Industrial, and Digital Photography Ap-
plications, 3965:392–401.
Drago, F., Myszkowski, K., Annen, T., and Chiba, N.
(2003). Adaptive logarithmic mapping for displaying
high contrast scenes. In Brunet, P. and Fellner, D. W.,
editors, Proc. of EUROGRAPHICS, Computer Graph-
ics Forum, pages 419–426. Blackwell Pub.
Felzenszwalb, P. F. and Huttenlocher, D. P. (2004). Effi-
cient graph-based image segmentation. International
Journal of Computer Vision, 59:167–181.
Frazor, R. A. and Geisler, W. S. (2006). Local lumi-
nance and contrast in natural images. Vision Research,
46:1585–1598.
Immerkær, J. (1996). Fast noise variance estimation. Com-
puter Vision and Image Understanding, 64:300–302.
Kuhna, M., Nuutinen, M., and Oittinen, P. (2011). Method
for evaluating tone mapping operators for natural high
dynamic range images. In Imai, F. H. and Xiao, F.,
editors, Digital Photography VII, SPIE Proc., pages
78760O–78760O–12. SPIE.
Ledda, P., Chalmers, A., Troscianko, T., and Seetzen, H.
(2005). Evaluation of tone mapping operators using
a high dynamic range display. ACM Transactions on
Graphics, 24:640–648.
Mantiuk, R., Tomaszewska, A., and Heidrich, W. (2009).
Color correction for tone mapping. In Dutr, P. and
Stamminger, M., editors, Proc. of EUROGRAPHICS,
Computer Graphics Forum, pages 193–202. Black-
well Pub.
Reinhard, E. (2002). Parameter estimation for photographic
tone reproduction. J. of Graphics Tools, 7:45–52.
Reinhard, E., Heidrich, W., Debevec, P., Pattanaik, S.,
Ward, G., and Myszkowski, K. (2010). High Dynamic
Range Imaging: Acquisition, Display, and Image-
Based Lighting. Morgan Kaufmann, 2nd edition.
Reinhard, E., Stark, M., Shirley, P., and Ferwerda, J. (2002).
Photographic tone reproduction for digital images.
ACM Transactions on Graphics, 21:267–276.
Smith, K., Krawczyk, G., Myszkowski, K., and Seidel, H.-
P. (2006). Beyond tone mapping: Enhanced depiction
of tone mapped HDR images. Computer Graphics Fo-
rum, 25:427–438.
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