Tone Mapping for Single-shot HDR Imaging

Johannes Herwig, Matthias Sobczyk and Josef Pauli

Intelligent Systems Group, University of Duisburg-Essen, Bismarckstr. 90, 47057 Duisburg, Germany

Keywords:

High Dynamic Range Imaging, Tone-reproduction Operators, Noise Reduction, Image Segmentation.

Abstract:

The problem of tone mapping for HDR (high dynamic range) to LDR (low dynamic range) conversion is

introduced by a uniﬁed framework considering all the usual processing steps. Then the speciﬁc problem of

single-shot HDR is outlined where special emphasis is taken on the effect of the greater noise ﬂoor of those

images when compared to the usual exposure bracketing approach to HDR. We herein tailor the popular tone

mapping operators proposed by Reinhard for single-shot HDR. A region-based approach for preprocessing

any HDR image in order to increase SNR and perceptual sharpness is introduced as an extension to our initial

tone mapping framework. The results are compared with respect to specially developed baseline tone mappers

and an extensive subjective evaluation is performed.

1 INTRODUCTION

Tone mapping operators are used in a high dynamic

range (HDR) image acquisition and processing chain

(Reinhard et al., 2010) as the ﬁnal completion. Tone

mapping allows displaying or printing a HDR image

on LDR (low dynamic range) media by compressing

the wide tonal range of the HDR image into an im-

age with lower tonal sampling. Thereby the bit-depth

of the HDR image pixel commonly is 32-bit ﬂoating

point but LDR images are only 8-bit unsigned inte-

gers. Tonal compression should be able to preserve

the overall contrast, textural details and color ﬁdelity

of the original image (Frazor and Geisler, 2006).

Often only a HDR image is capable of captur-

ing the real dynamic range of any natural scene, but

both consumer digital displays and printing technolo-

gies are only capable of dealing with low dynamic

range images (DiCarlo and Wandell, 2000). The crux

thereby is that photographers usually want to create

a ”true” reﬂection of their visual experiences which

they want to convey to their viewers, but due to lim-

ited capturing and displaying technologies any photo-

graph can never be as visually rich as the real scene.

Therefore, the tone mapper is crucial in delivering

an image that ”feels” as naturalistic as possible when

viewed at low dynamic range.

Although, tone mapping operators are developed

with the (in-)capabilities of the human visual system

(HVS) in mind, their design can be considered more

an art than engineering. This is also because of the

vast amount of factors that inﬂuence the sensation of

an image where lots of assumptions are to be made.

For example, apart from the tonal richness of the par-

ticular HDR image additional properties of the view-

ing conditions and the audience are to be considered:

speciﬁc display technology, viewing distance, ambi-

ent lighting, emotional state, cultural background, etc

(Bodrogi and Khanh, 2012).

In this paper, we present several extensions and

enhancements of the tone mapper for photographic

tone reproduction originally introduced by Reinhard

(Reinhard et al., 2002). Reinhard has developed dif-

ferent tone mapping operators in the past which are

commonly acknowledged for their naturalistic results

thereby advancing this ﬁeld of research (Reinhard

et al., 2010). His and other operators usually assume

that the HDR image was created by fusing a series of

differently exposed LDR images of the same scene.

1.1 Tone Mapping for Single-shot HDR

In our application scenario only one image is taken

with a pixel depth of 12-bit unsigned integer, which

means there are 4096 different values that we want

to tone map to 8-bit or 256 different values per color

channel. We use the so-called RAW imaging mode

of a digital camera which directly stores the raw but

color balanced pixels from the digital imaging sensor.

For our tone mapping we exploit the fact that mod-

ern digital consumer cameras internally have higher

analog-to-digital conversion (ADC) capabilities than

145

Herwig J., Sobczyk M. and Pauli J..

Tone Mapping for Single-shot HDR Imaging.

DOI: 10.5220/0004695401450152

In Proceedings of the 9th International Conference on Computer Vision Theory and Applications (VISAPP-2014), pages 145-152

ISBN: 978-989-758-003-1

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

HDR image

Scaling of

channels

[0-1]

Create

luminance

image

Modify

tonal range

Normalize

tonal range

[0-1]

Gamma

correction

(γ)

Color

reproduction

Mapping to

target range

LDR image

Tone mapping operator

Color information

Brightness information

Figure 1: General framework for tone mapping.

their low dynamic range JPEG output images. This

ultimately means that these cameras already have im-

plemented proprietary tone compression algorithms.

These are however restricted to the light processing

power of current imaging processors and therefore

cannot perform advanced computations. In our ex-

perience the qualitative results of these global tone

mappers generally come close to some sort of gamma

correction which is one of the simplest tone mappers

and does not sufﬁciently lighten up shadowed parts

and tends to wash out textural details in brighter parts

of the image. Our globally and locally adaptive ap-

proaches are designed to overcome these issues.

The single-shot HDR approach provides a lower

dynamic range than real HDR capture using multiple

exposures. It however is easier to apply to dynamic

scenes. On the other hand, a single-shot HDR im-

age is noisier because there is no averaging of mul-

tiple exposures and since tone mappers are designed

for compressing wider dynamic ranges than a single

shot provides the noise tends to be intensiﬁed because

its is assumed to represent textural detail.

Although there are lots of different tone mapping

algorithms these nevertheless can be described by the

framework depicted in ﬁgure 1. Note that we always

perform an additional gamma correction after tonal

compression in order to comply with the usual ITU −

RBT.709 HDTV-standard for displaying devices.

2 PREVIOUS WORK

2.1 Linear Mapping

With a linear mapping a given range of values is

mapped to some target range by scaling with a con-

stant factor. HDR images that are downscaled this

way generally appear too dark and textural details in

shadowed regions get lost. This can be accommo-

dated by pre-scaling the HDR image like this:

(y, x) =

I(y, x) − I

mincut

maxcut

− I

mincut

with I

maxcut

, I

mincut

∈ {v | v ∈ R ∧ 0 < v ≤ 1},

maxcut

> I

mincut

, I(y, x) ∈ {0.0, . . . , 1.0} and intensities

(y, x) > 1.0 and I

(y, x) < 0.0 will get clipped.

maxcut

could be set in such a way that e.g. the up-

per 5% of intensities of the HDR image get clipped,

i.e. are collectively set to the maximum value of the

target range. This will result in a brightened LDR im-

age because fewer higher intensity outliers have less

impact on the overall scaling factor. Although the re-

sulting effect of burning pixels within highly lit image

regions causes lost textural details, it is positive for

perception (Reinhard et al., 2010) and enhances the

overall image contrast. We experimentally found that

clipping the higher 1% of intensities does not result in

any visually perceivable loss of information.

mincut

can be either set to zero or it can be sim-

ilarily used in order to clip the noise within darker

regions of the image which is especially useful for

single-shot HDR and further contributes to preserv-

ing visually important textural details in the resulting

LDR image. Thus I

mincut

could be set to the estimated

noise level (Immerkær, 1996) of the HDR image.

2.2 Global Photographic Mapping

Reinhard has successfully introduced an operator

(Reinhard et al., 2002) that is inspired by the zone

system of the famous photographer Ansel Adams.

A zone system splits the tonal range of the HDR

image into 11 different tonal zones from pure black

(zone 0) to pure white (zone 10). Zones 1 − 9 have

pre-set brightnesses which linearly blend into each

other. Thus zone 5 is of medium brightness and the

so-called scene key α maps to this zone. If α is

of relatively low brightness, then the overall image

is brightened up so that shadowed regions are better

visible but lighter regions loose richness at the same

time. If α is of relatively high brightness, then shad-

owed regions fully loose detail but brighter image re-

gions feature more textural richness.

This zone system is realized by the following tonal

compression of the normalized luminance image I:

(y, x) =

(y, x)

1 + L

(y, x)



1 +

L(y, x)

maxcut

(y, x)



whereby L

(y, x) denotes the intensity I(y, x) which is

scaled by the scene key α:

(y, x) =

avg

· I(y, x)

with α ∈ {x | x ∈ R ∧ 0 ≤ x ≤ 1} that can automat-

ically be computed (Reinhard, 2002) from the loga-

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146

rithmic average I

avg

of the HDR luminance image as

α = 0.18 · 4

2·log

avg

)−log

mincut

)−log

maxcut

)

log

maxcut

)−log

mincut

)

2.3 Local Photographic Mapping

Based on the global tone mapping operator presented

in the previous paragraph, a localized version of this

operator is given in (Reinhard, 2002). This oper-

ator is inspired by the technique of ”dodging and

burning” that has also been originally developed by

Ansel Adams when making photographic prints on

paper that were not straightforwardly capable of the

full tonal range of his ﬁlm negatives. Here, every

pixel is selectively brightened up or darkened based

on an adaptively computed local neighborhood of

small enough intensity variance. Darker pixels that

are comprised by similar but somewhat lighter pixels

are damped more strongly than bright pixels that are

surrounded by relatively darker pixels, thereby creat-

ing higher local contrast in the tone mapped result.

This outlined the overall idea, but for more details the

reader is referred to Reinhard’s original publication.

3 PROPOSED APPROACHES

Based on the well-accepted algorithms of Reinhard

we propose the following extensions and enhance-

ments for single-shot HDR imaging.

3.1 Dynamic Scene Key

We present here the global photographic mapping

with a dynamic scene key α(y, x) that is different for

every image pixel. In the original algorithm α can

only be varied for the whole image, whereby an in-

creased α brightens the mid-tones but a decreased α

darkens the the whole image.

For improving the overall perception of image

contrast and simultaneously preserving or enhancing

local textural details it is only necessary to selectively

brighten up shadowed image regions but largely pre-

serve the local brightnesses of already bright image

regions in order to avoid the loss of visual detail. This

desired qualitative behavior of our proposed function

for computing α(y, x) is depicted by ﬁgure 2. Only

very bright intensities are starkly dampened and also

very dark intensities are smoothly cut-off since these

often tend to represent image noise. We experimen-

tally found the following adaptive α(y, x) which varies

with pixel intensity I(y, x):

α(y, x) = 1 − exp(−(I(y, x) · (1 + d · I(y, x)))

1−(I

avg

)

0.2

0.4

0.6

0.8

0.2

0.4

0.6

0.8

0.2

0.4

0.6

0.8

I(y, x)

avg

α(y, x)

Figure 2: The qualitative behavior of our varying scene key

α(y, x) with recommended c = 4. For better visualization

variable d is constantly set to 1 here.

with the user deﬁned c ∈ {x | x ∈ N ∧ x > 0} and

d =



−1 if I(y, x) < I

avg

1 if I(y, x) ≥ I

avg

The constant c determines the overall strength

of the contrast enhancement. Thereby smaller val-

ues create greater contrast as is depicted in ﬁgure 3.

Note that only the shadowed image regions are starkly

brightened up with increasing c but that already bright

image regions are only moderately brightened which

works out as expected. This property makes the only

user parameter for computing α(y, x) to be robust for

a large amount of different scenes.

In our experiments a value c = 4 has been proven

to produce pleasing results for most scenes. For dif-

ferent scenes with an overall dark appearance the re-

sulting α is always between ≈ 0.63 and ≈ 0.86, but

for scenes with higher average brightnesses it usually

lies between ≈ 0.63 and ≈ 0.65.

3.2 Modiﬁed Local Photographic

For single-shot HDR we experienced that Reinhard’s

local photographic tone mapper can be safely used

with its default parameters because the comparatively

low dynamic range of RAW camera images does not

take advantage of those. Also shadowed regions are

adequately brightened up.

But we found that there is severe burning-in

among brighter parts of the scenes which causes su-

perﬂuous loss of detail and local contrast. Here we did

not change the original operator itself but the method

for normalizing the tonal range which is the ﬁrst post-

processing step as depicted in ﬁgure 1. Here the stan-

dard method is to simply cut-off values I

(y, x) > 1.0.

After tonal compression, we propose to project all

the values I

(y, x) which are out of range into the valid

ToneMappingforSingle-shotHDRImaging

147

Figure 3: This image series shows the effect of parameter c (= 1, 4, 6 and 11) of the dynamic scene key α(y, x).

0.0, . . . , 1.0 range:

normalized

(y, x) =

(y, x) − I

min

max

− I

min

Thereby I

min

denotes the smallest and I

max

is the

largest value of I

(y, x). In order to avoid that large

values for I

max

result into high loss of information

in darker image regions, we additionally dampened

these by using the square root. Therefore very large

brightness values (>> 1.0) are more starkly damp-

ened than smaller out of range values (> 1.0). This

also avoids that the linear scaling by I

normalized

(y, x)

produces overly dark images.

This minor modiﬁcation has only an effect if the

original tone mapper produced values I

(y, x) > 1.0.

Then the overall contrast of the result images gets re-

duced but at the same time local detail in bright re-

gions is enhanced as intended. The result is com-

parable to the global photographic mapping without

burning-in, but shadowed regions are more intense

here which was the original beneﬁt of the local pho-

tographic mapping approach.

3.3 Region-based Preprocessing

The previously presented methods already result into

visually pleasing results. The problem with tone map-

ping single-shot HDR images is however the extended

noise ﬂoor when compared to bracketed HDR expo-

sure series. Therefore we extend the usual tone map-

ping framework of ﬁgure 1 with a universally appli-

cable pre-processing step. Here we want to selec-

tively increase the signal-to-noise ratio (SNR) of low-

lit and therefore noise-prone image regions by using a

smoothing ﬁlter. At the same time we want to increase

perceived image sharpness in areas with already orig-

inally satisfactory SNR in order to enhance textural

detail. The image segmentation occurs on the HDR

luminance image as depicted in ﬁgure 4.

For both smoothening an sharpening we use the

same ”unsharp masking” algorithm, which is a com-

mon tool in image processing software. Thereby we

use the ﬁlter mask I

which is the HDR input image

I that is convolved with a 3 × 3 Gaussian smoothing

HDR image

Scaling of

channels

[0-1]

Create

luminance

image

Modify

tonal range

Normalize

tonal range

[0-1]

Gamma

correction

(γ)

Color

reproduction

Mapping to

target range

LDR image

Tone mapping operator

Color information

Brightness information

Create

segmentation

Smoothen/

sharpen

luminance

Figure 4: Extended framework for tone mapping.

kernel with σ = 0.8:

SMOOT H(I, a) = I · (1 + a) +I

· (−a) with a < 0

SHARPEN(I, a) = I · (1 + a) + I

· (−a) with a > 0

and a ∈ R. When smoothening the SNR is increased

but local contrast is necessarily reduced, we however

tackle single-shot noise in order to improve the over-

all quality in darker image regions. Sharpening on

the other hand always reduced SNR but increases the

visual perception quality of textural detail in the im-

age. Sharpening is always applicable to global tone

mapping operators, whereas most local tone mapping

operators already perform some inherent sharpening

(Mantiuk et al., 2009), so that additional sharpening

may overdo the intended perceptual effect.

Here we use a popular graph-based segmentation

algorithm (Felzenszwalb and Huttenlocher, 2004) that

was extended for coping with HDR images. This

greedy algorithm has three parameters: (1) the sim-

ilarity measure K controls which neighboring pixels

will belong to the same region, (2) the constant min

denotes the minimum number of pixels of every re-

gion, (3) and σ is the smoothing parameter of a Gaus-

sian pre-ﬁltering step. We found experimentally that

parameters K = 0.36, min = 10 and σ = 1.6 pro-

vide good enough segmentation results. Note that for

our purpose no exact segmentation is needed. How-

ever, an over-segmentation is preferable over under-

segmentation. Some segmentation results with differ-

ent parameters are exemplarily shown in ﬁgure 5.

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148

Figure 5: The graph-based segmentation leads to under-

segmented, acceptable, and over-segmented results, resp.

In the following we describe the targeted process-

ing that is chosen for every segmented image region s

based on different quality measures:

1. For small standard deviation: replace pixel values

within this segment with its average intensity.

0 ≤ I

≤ σ

noise

(y, x) = I

avg

2. For small entropy: replace pixel values within this

segment with its average intensity.

0 ≤ I

≤ σ

noise

(y, x) = I

avg

3. For small SNR: smooth this segment.

0 ≤ I

SNR

≤ 30 SMOOT H(I

, −1)

4. For mean SNR: moderately sharpen this segment.

30 ≤ I

SNR

≤ 39 SHARPEN(I

, 1)

5. For high SNR: starkly sharpen this segment.

SNR

≥ 39 SHARPEN

, 1.5)

The decision parameters have been found by

excessive testing with a wide variety of images.

These were chosen to avoid negative perceptual ef-

fects around the borders of image regions where a

smoothed region is adjacent to a sharpened image re-

gion at normal viewing distances. The trained eye

however can experience minor visual artifacts when

the image would be enlarged. These are not disturb-

ing but could however be further dampened by apply-

ing additional blending techniques.

In this paper, we evaluate the effect of this region-

based preprocessing by the global photographic and

the simplistic linear tone mapping operator. Thereby

the normalization of the compressed tonal range (see

ﬁgure 4) always cuts off out of range values. When

comparing the results of the tone mapping with and

without region-based preprocessing there visually oc-

cur only minor differences between the results and

also the global contrast does not change which is as

intended. The local contrast is however slightly in-

creased. Depending on the amount of smoothed ver-

sus sharpened image regions the SNR either increases

or decreases, respectively. This is as intended, be-

cause sharpening adds more edges as textural detail

but is falsely interpreted as noise by the SNR measure,

and on the other hand smoothing increases SNR.

3.4 Alternating Global and Local

Photographic Mapping

As has been mentioned previously, local tone map-

ping operators often inherently perform some form of

sharpening the image (Mantiuk et al., 2009). How-

ever, sharpening is not desired for noise image regions

because noise will be unnecessarily enhanced. Since

the local photographic tone mapping operator can be

reduced to Reinhard’s original global version, we pro-

pose to alternatingly use both of these depending on

the local SNR of an image region.

Thereby we make use of our previously intro-

duced graph based segmentation as follows:

1. For small SNR: process with the global operator.

0 ≤ I

SNR

≤ 30 R(I

,V (s

min

), 0)

2. For mean SNR: process with the local operator

with moderate sharpening.

30 ≤ I

SNR

≤ 39 R(I

,V (s

max

), 8)

3. For high SNR: process with the local operator

with starker sharpening.

SNR

≥ 39 R(I

,V (s

max

), 16)

Here, function R encapsulates the parameterization of

the local tone mapping operator (Reinhard, 2002) as

R(segment, neighborhood, sharpening). Hence, the

ﬁrst action uses the smallest Gaussian neighborhood

V (s

min

) of size 1, which effectively transforms the

local mapping operator into its global counterpart.

Whereas the two remaining actions select the largest

possible Gaussian neighborhood V(s

max

) but use dif-

ferent amounts of sharpening 8 and 16, respectively.

This approach results into minor enhancements of

the SNR when compared to the original approaches,

and also the correlation coefﬁcient between the orig-

inal HDR and the resulting LDR image is increased.

Furthermore, artifacts that occurred at borders of dif-

ferent regions as with the previous region-based ap-

proach are not seen here.

3.5 ”Comic” Algorithm

This is an artistic mapping that we developed as a neg-

ative baseline that helps to better interpret the results

in our evaluation section. Its output is not naturalistic

and does not adhere to the goals of this paper.

First, we compute the cosinus-weighted RMS

contrast as in (Frazor and Geisler, 2006). This local

contrast measure is computed over a 3 × 3 neighbor-

hood for every pixel and the resulting image is de-

noted I

. The resulting pixels are normalized between

0.0 and 1.0 and inverted, so that originally small local

contrasts result into larger values than originally high

ToneMappingforSingle-shotHDRImaging

149

local contrasts. This inverted I

−1

is then transformed

by a common histogram equalization resulting into

−1,eq

where the probabilities of occurrence of all the

available intensities are approximately equal. Since

we perform histogram equalization on ﬂoating point

HDR luminances, we have scaled intensities by 10

so that the resulting bins are meaningful and not

mostly empty. In the last processing step, we scaled

the pixels of the here computed contrast image I

−1,eq

with the unprocessed HDR luminance I:

(y, x) =

I(y, x) · I

−1,eq

(y, x)

Since noise is greatly enhanced due to the contrast

inversion, we convolved the original luminance HDR

with a 11 ×11 box-ﬁlter before the color reproduction

step in the processing framework of ﬁgure 1.

The resulting tone mapping traces textural board-

ers between homogeneous image regions and there-

fore is termed the ”comic” operator. The resulting

LDR image features high local contrast and very low

SNR. This properties make the algorithm suitable as

a baseline for the evaluation and comparison of other

tone mapping operators.

4 EVALUATION

The evaluation of tone mapping operators often

takes place in photometrically calibrated environ-

ments (Kuhna et al., 2011), (Ledda et al., 2005). This

is very complex to set up and the comparison results

are practically questionable because ordinary users do

not have calibrated monitors and also the environmen-

tal effects on image perception can never be canceled

out. Therefore, we propose a quantitative evaluation

with baseline operators in the evaluation set in order

to arrange the results. Here we use our ”comic” oper-

ator and the adaptive logarithmic (Drago et al., 2003)

operator as tone mappers producing extreme results

at both ends of the evaluation scale. Whereas the

”comic” operator produces extremely high contrast

and noisy results, the operator by Drago produces

very dull but highly detailed results. We think that

both results are not perceptually preferable and hence

a good algorithm should produce results in-between.

There are 27 single-shot HDR images in our eval-

uation set featuring a broad range of scenes with

higher and lower overall contrast and more or less

scenic details. Due to space constraints we present

only cropped results from two different scenes in ﬁg-

ure 6 obtained for every algorithm.

(a) Linear with 5% cut-off

(b) Global photographic without burning

(d) Dynamic scene key

(e) Local photographic

(f) Local photographic with modiﬁed normalization

(g) Region-based global photographic without burning

(h) Region-based with adaptive unsharp masking

(i) Region-based with linear mapping

(j) Comic algorithm

(k) Adaptive logarithmic (by Drago)

Figure 6: Cropped images from LDR results of various tone

mapping algorithms as described in this paper.

4.1 Quantitative Evaluation

For the quantitative evaluation we chose the follow-

ing criteria: correlation ratio, signal-to-noise ratio,

global contrast, and local contrast. The correlation

ratio measures the correlation between an LDR im-

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150

SNR

Figure 7: Average SNR values and standard deviations of

LDR images. The shaded area denotes the SNR and stan-

dard deviation of the original HDR images for comparison.

0.05

0.1

0.15

0.2

0.25

0.3

RMS (G)

Figure 8: Average RMS contrast and standard deviations of

LDR images. The shaded area denotes the RMS and stan-

dard deviation of the original HDR images for comparison.

age and its original HDR scene by using the Pearson

coefﬁcient. Global and local contrast were both mea-

sured using the RMS contrast approach (Frazor and

Geisler, 2006), whereby the global contrast was mea-

sured for the whole image and the local contrast result

is the average of multiple windowed RMS measure-

ments. Results of the SNR and global contrast mea-

sures are depicted in ﬁgures 7 and 8 (please refer to

ﬁgure 6 for identifying the different algorithms). It

can be noted that the results greatly differ from each

other, but that there is no clear winner, so trade-offs

have to be made. In the ﬁgures, however, we have

also indicated the variance of those quantitative mea-

sures within the original image set of 12bit data. We

have experienced that a good tone mapping algorithm

should have values whose mean lies well within this

shaded stripe, and whose top values perform little bet-

ter than on the original data although their range of

variance should preferably be small.

4.2 Qualitative Evaluation

We chose a small control group of ﬁve students who

are experienced in image processing. Every tone

mapped LDR image was rated with values between 0

for poor and 5 for exceptional performance within the

following qualitative categories: brightness, global

contrast, local contrast, textural details, artifacts, ﬁ-

delity, and naturalness. It is however very difﬁ-

cult to distinguish between some of these categories

because participants sometimes have different ideas

about those concepts like contrast vs. textural details.

Figure 9: Average subjective naturalness and standard devi-

ations of LDR images.

We exemplarily show the results for the subjec-

tively perceived naturalness in ﬁgure 9. To summarize

all subjective results we compiled a table of the ﬁnal

ranks. We chose to create composite measures where

we evaluate the naturalness with respect to other de-

sired image features like contrast (global and local)

and textural detail. From table 1 it can be concluded

that algorithms f and d (compare with ﬁgure 6) show

good overall perceptual performance. It is interesting

to note that algorithm f did not modify the original al-

gorithm e but only the tonal normalization: as can be

seen from the table, this had a great effect on its rank.

Algorithm d is based on b with the intend to enhance

the global contrast which greatly succeeded. At the

same time the detail reproduction of d is worse then

that of b which is a direct consequence of increasing

global contrast (Smith et al., 2006), and the evaluation

data exactly reﬂects that.

4.3 Discussion

According to ﬁgure 9 algorithms d, e and f perform

best on average concerning the perceived naturalness,

whereby d and f also show a reasonably small vari-

ance over the whole set of images. These two algo-

rithms are also ranked best in our comparison table 1

whereby d creates higher perceived contrast but f is

better balanced between image contrast and preserv-

ing textural detail. These characteristics are veriﬁed

by our quantitative measurements where d is second

best in terms of RMS contrast as shown in ﬁgure 8 and

ToneMappingforSingle-shotHDRImaging

151

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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