IEEE SPM MAGAZINE, VOL. 34, NO. 5, SEPTEMBER 2017

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High Dynamic Range Imaging Technology
Alessandro Artusi, Thomas Richter, Touradj Ebrahimi, Rafał K. Mantiuk,

Acquisition

arXiv:1711.11326v1 [cs.GR] 30 Nov 2017

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N this lecture note, we describe high dynamic range
(HDR) imaging systems; such systems are able to represent
luminances of much larger brightness and, typically, also a
larger range of colors than conventional standard dynamic
range (SDR) imaging systems. The larger luminance range
greatly improve the overall quality of visual content, making
it appears much more realistic and appealing to observers.
HDR is one of the key technologies of the future imaging
pipeline, which will change the way the digital visual content
is represented and manipulated today.

Storage/Compression Display
LDR Display

Real Scene
LDR Camera
Viewing Conditions 2

Encoding-Decoding
Tone Mapping

Observer 1

Observer 2

Viewing Conditions 1
Native Visualization

HDR Camera
CG Modeling

CG engine

HDR Display

HDR Quality Metrics
Objective metric

Subjective

I. P REREQUISITES
Essential knowledge of linear algebra, image/signal processing and computer graphics is desirable. The basic aspects of
High Dynamic Range (HDR) imagery are required for the full
comprehension of this lecture note. The readers are invited
to consult [1] for acquiring this basic know-how before to
proceed with the reading of this lecture note.
II. R ELEVANCE
Due to the availability of new display and acquisition
technologies, interest in HDR increased significantly in the
past years. Camera technologies have greatly evolved providing high quality sensors that generate images of higher
precision and less noise; the market offers now displays that
are able to reproduce content with higher dynamic range,
peak luminance and color gamut. These advances are opening
a large number of applications that span from broadcasting
to cinema, manufacturing industry to medical. This is also
demonstrated by activities taking place nowadays within the
standardization communities, i.e., JPEG, MPEG and SMTPE.
New standards have been created for still HDR images, i.e.,
ISO/IEC 18477 JPEG XT [2], others are under development.
All these activities are largely driven by industry, a strong
indication that business cases around HDR will emerge in the
near future.
III. P ROBLEM S TATMENT AND S OLUTION
A. Problem Statment
The problem to be solved consists of the development of an
imaging and video system pipeline capable of representing a
wider range of luminance and colors values compared to the
traditional, standard dynamic range (SDR) system pipeline.
The idea is to design a complete system, which incorporates
acquisition, storage, display and evaluation subsystems, as
shown in Figure 1.
DOI: 10.1109/MSP.2017.2716957 ˜ c 2017 IEEE.

Fig. 1. HDR imaging pipeline: acquisition (greyish), storage (violet), display
(yellowish) and evaluation (greenish).

B. Solution
1) Acquisition: Two major ways exist to generate HDR
content, either generating scene through computer graphics
tools or through the acquisition of real world scene with a
camera. Rendering pipelines for computer-generated graphics
integrate tools such as physically-based lighting simulations
that use physical-valid data of the scene and the environment,
i.e., light sources and object materials. The models used there
are capable of simulating physically plausible behavior of the
light of the scene within a specific environment and generate
plausible images from an abstract scene description.
The second method acquires HDR images from real-word
scenes; today, high quality digital single-lens reflex (DSLR)
cameras are available with sensors capable of capturing 12to-16-bits per color channel. However, many portable devices,
such as mobile phones and lower quality digital cameras are
equipped with cheaper, lower performing hardware whose
precision is limited to 10 bits or even lower.
For such a device, only a small subset of the available
dynamic range of the scene can be captured, resulting in
overexposed and underexposed areas of the acquired image.
To overcome this limitation, one can capture different portions
of the dynamic range of the scene by varying the exposure time. The resulting images are then first registered, i.e.
aligned to each other, before a camera response function is
estimated from them. This function describes, parametrized
by the exposure time, how luminances received by the sensor
are mapped to pixel values; its inverse allows to estimate
physical quantities of the scene from the acquired images.
Finally, a weighted average over the pictures generates an
HDR image [3]. The selected weights indicate the contribution
of each frame at a given position to the final HDR sample
value. An example of multi-exposure approach is depicted in

IEEE SPM MAGAZINE, VOL. 34, NO. 5, SEPTEMBER 2017

Fig. 2. Multi-exposure approach used to capture an HDR image - (left) three
images taken with three different exposure times t1, t2 and t3, with the
different portions of the dynamic range of the scene captured by the exposure
time ti - (right) the reconstructed HDR image. The reconstructed HDR image
is tone mapped for display purposes.

Figure 2; here three images of the same scene were taken,
varying the exposure time. A (tone mapped) version of the
resulting HDR is also depicted. A typical problem of the multiexposure method is the misalignment of the images, either due
to movements in the scene or by the camera itself [4]. Merging
such images without further processing results in ghosting
artifacts in the HDR output. Here below, such defects can be
classified as follows:
• Global misalignment due to camera motion, e.g. camera
movement or rotation. This type of misalignment affects
all pixels of the image causing ghost artifacts that can be
removed through image registration.
• Local misalignment due to moving objects in the scene,
only affecting portions of the image. Such defects arise if
the time between the individual exposures is larger than
the typical time within which an object moves in the
scene. For example, some objects may be occluded in
one of the images, but are visible in others.
• Local and Global misalignments combining the two
previous types. A typical example is that of a camera
that follows a free path, acquiring a scene composed of
dynamic objects.
2) Storage and Compression: A naı̈ve analysis of HDR
images reveals that uncompressed HDR data would typically
require four times more storage capacity than SDR data.
Clearly, this view is oversimplifying the situation, but it
should at least indicate the need for a data format that is
more compact. Various better alternatives exist in the field,
amongst them half-float (as used by OpenEXR), RGBE,
LogLuv encoding, and representation of sample values in a
perceptually uniform color space through an electro-optical
transfer function (EOTF). All these convert a direct, floating
point representation into a more efficient data format that
requires less bits, while still providing a higher dynamic range
and more precision than an SDR format.
If the precision of the HDR format is not sufficient, then
quantization defects such as banding will become visible.
We now discuss a selection of popular HDR formats. Halffloat precision is a compact representation for floating point
values where one bit is used for the sign, 5 bits for the
exponent and 10 bits for the mantissa. The advantage that the
half-float representation is offering is that it is as flexible as
the regular single precision floating point format at half of the

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storage cost. However, since the maximum value representable
by this format is 65535, sample values should be calibrated by
a common scale factor, i.e., represented in “relative radiance”
to be able to represent the full dynamic range in the image.
The RGBE format takes advantage of the fact that the color
components of an RGB image are highly correlated and that
they have usually very similar magnitude. RGBE thus only
stores one common scale factor for all three components in
the form of an exponent E, and the individual channels are
jointly scaled by E as follows:
Re = b

256R
c,
2E−128

(1)

for G and B the same equation applies. The b.c denotes
rounding down to the nearest integer. E is the common
exponent that is encoded together with the RGB mantissas,
resulting in a 32-bit per pixel representation.
E = dlog2 (max(R, G, B)) + 128e,

(2)

where d.e denotes rounding up to the next integer.
A drawback of RGBE pixel encoding is that it cannot
represent negative samples values, i.e., colors that are outside
of the triangle spanned by the primary colors of the underlying
RGB color space. A possible remedy is to code colors in the
XYZ color space taking only positive numbers by definition,
then giving rise to the XYZE encoding.
In both cases, however, errors are not uniformly distributed
perceptually speaking, a problem that is partially solved by
the LogLuv encoding. There, the luminance is coded logarithmically in one sign bit, 15 mantissa bits and another 16 bits
to encode the chroma values ue and ve .
Logarithmic encoding of luminance values is a common
trick used in many HDR encodings: When the same magnitude
of distortion is introduced in low and high luminance image
regions, one finds that artifacts will be more visible in low
luminance regions as human vision follows approximately a
logarithmic law — this is also known as Weber’s Law in the
literature.
However, more accurate models of human vision exist that
map physical luminance (in nits, i.e., candela per square meter)
into the units related to the just-noticeable-differences (JNDs).
Such a mapping, namely from perceptually uniform sample
space to physical luminance, is also denoted as electro-optical
transfer function ( EOTF). Studies have shown that under such
a mapping 10 to 12 bits are sufficient to encode luminances
between 10−4 to 108 nits without visible banding.
HDR file formats that are making use of these HDR pixels
representation have been proposed, and the three most widely
used are Radiance HDR, the Portable File Format (PFM) and
OpenEXR. Radiance HDR, indicated by the file extension
.hdr or .pic, is based on RGBE or XYZE encoding, plus
a minimal header. A very simple run-length coding over rows
is available.
PFM is part of the “portable any map” format, and is indicated by the .pfm extension. The header indicates the number
of components and a common scale factor of all sample values;
the sign of the scale factor denotes the endianness of the

IEEE SPM MAGAZINE, VOL. 34, NO. 5, SEPTEMBER 2017

encoding. The actual image pixels are encoded as RGB triples
in IEEE single precision floating point.
OpenEXR uses as file extension .exr, and it has been
developed by Industrial Light and Magic in the 2002, along
with open source libraries. Due to its high adoption it has
become the de-facto standard file format for HDR images,
especially in the cinema industry. This file format supports
three pixel encoding formats: half-float (16-bit float), 32-bit
float and 32-bit integer. It also includes various lossy and
lossless image compression algorithms.
We recently see the adoption of HDR technologies into
products such as cameras with improved sensors, displays
providing higher dynamic range and/or larger color gamut.
Unfortunately, interoperability at device level is still at its infancy, making it difficult to exchange images between various
devices, or various vendors which try to lock-in customers
through proprietary formats [2].
As for images, two international standards are already available that support HDR content, namely ISO/IEC 15444, ITU-T
T.800 JPEG 2000 and ISO/IEC 29199, ITU-T T.832 JPEG XR.
Despite the fact that they support lossless compression, their
limited adoption by the market may be correlated with their
lack of backward compatibility with existing JPEG ecosystem [5]. Industry players are typically reluctant to change
technology in their production pipeline to cope with adoption
of newly established standards. A migration path from existing
to new solutions, allowing a gradual transition from old to new
technology helps them to keep the investments low.
To address this issue, the Joint Photographic Experts Group
(JPEG) formally known as ISO/IEC JTC1/SC29/WG1, began
in the 2012 the standardization of a new standard technology
called ISO/IEC 18477 JPEG XT [2]. The JPEG XT image
coding system is currently organized into nine parts that define
the baseline coding architecture (the legacy JPEG codestream
8-bit mode), an extensible file format specifying a common
syntax for extending the legacy JPEG, and application of this
syntax for coding integer or floating point samples between 8
and 16 bits precision [2].
This coding architecture is then further refined to enable
lossless and near-lossless coding, and is complemented by
an extension for representing alpha-channels [2]. Due to

Fig. 3. The simplified decoding workflow for JPEG XT standard[5].

B is the base layer and is always represented as a JPEG codestream
with 8-bit per sample. E is the extension layer that used in conjunction
with B allows the reconstruction of the HDR image.

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its flexible layered structure, the JPEG XT capabilities can
be extended into novel applications such as omnidirectional
photography, animated images, structural editing as well as
privacy and security that are under examination and development [6].
In practice, JPEG XT can be seen as a superset of the 8-bit
mode JPEG where existing JPEG technology is re-used whenever possible; this, in particular, allows to encode an HDR
image purely on the basis of legacy JPEG implementations.
JPEG XT is a two layered design, of which the first layer
represents the SDR image. It is encoded in JPEG, with 8-bits
per sample in the ITU BT.601-7 RGB color space (base layer
B), see Figure 3. The extension layer E includes the additional
information to reconstruct the HDR image starting from the
base layer B.
Concerning video compression, some recent standards are
providing options to encode video in high bit precision, i.e.,
up to 12 bits for ISO/IEC 14496-2 and ISO/IEC 14496-10
AVC/H.264. These modes are defined in the profile Fidelity
Range Extensions (FRExt), and for ISO/IEC 23008-2 ITU-TH.265 (HEVC) in the Format Range Extension (RExt).
The H.264/AVC extensions build upon an EOTF that covers
a dynamic range of up to 2.5 magnitudes; while sufficient for
consumer applications, this is a limitation for typical HDR
content.
H.265/HEVC recently integrated a transfer function for
HDR video content that pre-quantizes data to a 10 or 12 bit
domain which is then taken as input by the HEVC encoder.
This EOTF, denoted as ST2084 — Hybrid Log-Gamma — is
designed for luminances up to 10, 000 nits. Finally, guidelines
on how to encode HDR video content with HEVC, have been
provided in ISO/IEC 23008-14 and 15.
Similar to JPEG XT, a backward compatible solution for
HDR video encoding has been presented by Mantiuk et
al.[7]. Recently a signaling mechanism to support backward
compatibility, has been integrated into the HEVC standard
(ISO/IEC NP TR 23008-15). The backward-compatibility is
achieved as in the case of HDR still image encoding described
above. A base layer encodes the SDR frames, and an extension
layer hidden from the base includes the necessary information
to extend the dynamic range. To improve encoding performance, the redundancy information is minimized through the
decorrelation between the SDR and HDR streams, achieving
a reduction in size of the HDR stream to about 30% of the
size of the SDR stream. Invisible noise reduction is also used
to remove details that cannot be seen in the residual stream
prior to encoding.
3) Display: The native visualization of HDR content is
limited by the physics of the display. Despite the fact that the
current technology on the market can guarantee high contrast
ratio, this is achieved by lowering the black level. However,
the peak luminance remains limited, restricting the available
dynamic range for bright images. Even with enhanced contrast,
many display panels offer only a limited precision of 8 or at
most 10 bits per color channel, and not all of them support a
wide color gamut neither.
Tone mapping is a process that compresses the dynamic
range of an input signal to that available by the display or the

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Local

Global

defects. [9].

Fig. 4. Global vs Local - Global approach results in lost of details in high
contrast regions. The HDR frame is tone mapped for display purposes.

printing process while keeping the visualization convincing.
Tone mappers can be roughly classified into global and local
approaches. The former is applying the same tone curve on
the all image pixels. The latter takes the spatial position and
its surrounding into account; by that, local operators can take
advantage of known effects of the human visual system such as
local eye adaption to the luminance. While the former is simple
and efficient, it may fail to reproduce details in high contrast
image regions (see Figure 4 (top-left)). Although the latter can
reproduce details in such regions better, see Figure 4 (bottomleft), it often comes at the cost of increased complexity and
computational time; it may also introduce artifacts around
edges.
Despite this classification, we may also categorize the tone
mappers based on their intent. Three main categories of tone
mappers can be identified: based on visual system, for scene
reproduction and for best subjective quality. The first aims
at integrating into the tone mapper mechanisms that simulate
various aspects of the human visual system. This includes
glare, luminance and chromatic adaptations, night vision, etc.
The second category attempts to reproduce the best match in
color gamut and dynamic range available for the display on
which the image will be visualized. This is achieved through
the preservation of the original scene’s appearance. The last
category produces images with most preferred subjective quality. Typical examples are operators with parameters that can
be adjusted to achieve a specific artistic goal.
Color Correction Dynamic range mismatches between the
HDR data and display devices, as previously shown, are
typically handled by tone mappers, focusing on one dimension of the color gamut, along the luminance direction. This
generates two major drawbacks. First, appearance effects are
often ignored, leading to images which may appear poorly or
too saturated as shown in Figure 5 (left) [8]. Second, such
a tone mapper may not guarantee that all the sample values
of the tone mapped image are within the available target, as
shown in Figure 5 (right). Even though the output luminance
may be reproducible by the display, the chrominance may fall
out of the available gamut, resulting in clipping of extreme
colors. This clipping may again introduce hue shifts and image

Fig. 5. Tone mapper drawbacks - (left) changes in appearance due to either
a reduction or an excessive saturation, - (right) pixels may be within the
destination gamut only for the lightness channel L∗ ; however, their chroma
channel may still be out of gamut. Here the HDR input image has been tone
mapped for display purposes. Left image courtesy of Francesco Banterle and
right image courtesy of Tania Pouli.

To improve the saturation of the tone mapped image, a
simple solution is to introduce an adjustable parameter which
allows to control the overall saturation of the tone mapped
image [10]. In the following, let p be a parameter in [0, 1],
then
 p
Io
It =
Lt ,
(3)
Lo
Here Io is the input HDR image, It is the final output tone
mapped image (both in RGB values), Lo is the luminance of
the original HDR image and Lt is the luminance of the tone
mapped image. The parameter p then needs to be selected for
the best — most pleasing — result. Unfortunately, the simple
solution presented above does not only adjust the saturation, it
also implies a luminance shift. Controlling p to get the desired
effect may be hard. This problem can be overcome by a more
careful choice of the input and output scaling operation [10]:



Io
− 1 p + 1 Lt .
(4)
It =
Lo
While this allows better control of the luminance shift, it
may cause undesirable hue artifacts [8] if applied separately
to each component of an RGB image. The value of p in
the above equations can be automated based on the slope of
the tone curve at each luminance level [10]. To reduce hue
and lightness shifts, one may work with perceptual uniform
color space to separate the color appearance parameters such
as saturation from hue and lightness. This will allow to
modify the saturation, of the tone mapped image, to match the
saturation of the input HDR image while hue and lightness of
the tone mapped image It will remain untouched [8]. Other
approaches exploit the use of color appearance models,and
extend the concept of gamut mapping of the HDR content [9].
The former approach guarantees the matching of the color
appearance attributes between the input HDR and the tone

IEEE SPM MAGAZINE, VOL. 34, NO. 5, SEPTEMBER 2017

mapped images. The latter ensures that all the tone mapped
pixels are within the color gamut of the display, minimizing
the hue and luminance distortion.
Inverse Tone Mapping The latest standardization trends
and technological improvements push the display features
towards ultra HD, higher dynamic range (HDR), i.e., up to
1, 000 and 6, 000 nits, and wide color gamut (ITU-R Rec.
2020). Since traditional LCD panels with constant backlight
illumination are not able to reproduce the necessary dynamic
range, HDR displays make use of a modulated backlight.
In such a display, a front-layer LCD panel includes the
color filters and provides the necessary level of details for
accurate image reproduction and a lower resolution matrix
of independently controlled LEDs modulate its illumination
at a coarser level, providing a much larger dynamic range.
Optical layers and reflectors around each LED maximize the
brightness in its corresponding area of the front LCD panel
and minimize the light leakage into adjacent cells. Due to the
coarser resolution of the back panel image quality degradation
may appear, which can be reduced through the use of post
processing filtering of the displayed image.
The widespread availability of SDR content and the recent
availability of displays with larger dynamic range also made
it attractive to process such content for presentations on HDR
displays. This process can be seen as the opposite problem of
tone mapping, and is thus called “inverse tone mapping”. The
ability to reconstruct the mapping between the pixel values
encoded in the SDR image and the scene luminance values,
also known as inverse camera response function, is the desirable goal. While it is an easy task to reconstruct the camera
response function from a series of different exposures of the
same SDR content, it is an ill-pose problem to reconstruct
such an inverse when only a single exposure of an unknown
camera is available.
The camera response function models the complete pipeline
from light acquisition to SDR pixel values, including the (nonlinear) sensor response, exposure, camera post-processing (e.g.
flare-removal) and tone mapping of raw pixel values to SDR
sample values. Recovering the dynamic range for an SDR
content will consist of two basic steps. First, estimate an
inverse camera response function to linearize the SDR content
signal, then adjust the dynamic range of the SDR pixel to fit
it to the dynamic range of the HDR display. However, SDR
images are presenting two major issues when expanding them
to larger dynamic range. First, the limited pixels precision, i.e.,
quantization to 256 values per channel, causes loss of detail
and posterization. These artifacts while barely visible in the
SDR domain, can be emphasized during the expansion of the
dynamic range. Second, under and over-exposed regions in the
SDR image contain very limited information. This may lead,
during the dynamic range expansion, to regions that have the
same appearance as in the original SDR image.
To solve the first problem, advanced filtering is needed
before boosting the dynamic range of the SDR image. Bilateral
filtering is an example: by tuning its parameters properly, highand low frequencies can be processed separately avoiding
some of the typical artifacts of range-expansion. While lost
image content cannot be recovered in any way, to solve the sec-

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ond problem, inpainting may at least generate plausible image
details in under- or overexposed image regions, provided the
regions are sufficiently small and enough details are available
around them.
4) HDR Quality Indices: The evaluation of the quality of
an image or video is one of the fundamental steps in understanding whether the algorithm is capable of achieving a level
of quality acceptable for a specific application. Depending
on whether the original source is available when assessing
a somewhat distorted image or video, one distinguishes between “full reference” and “no reference” quality indices. If
only partial information on the original is available, they are
called “reduced reference” indices. In a second dimension, we
can distinguish between “objective” and “subjective” quality
indices. In the former method, a computer algorithm quantifies
the differences between a reference and a test image or video.
Such an algorithm may include a model of the human visual
system, and then evaluates the visibility of image defects in
terms of its observer’s model. The latter method, evaluates
quality through studies by human observers. Based on a
particular test methodology, observers are asked to qualify
characteristics of single or pairs of visual stimuli in form of
image or video and to provide a score on a scale, or a relative
rating between multiple presentations. Certainly, the second
method is capable of catching all aspects of human vision and
is thus more appropriate to evaluate (or even define) the quality
of an image or a video. It is, however, also very resource and
time consuming and only a limited number of media artifacts
can be rated by such a method. Objective quality indices,
as computer implementations, are more convenient as they
allow automatic assessment. However, they are less reliable
in estimating the overall image quality as their assessment is
based on a limited mathematical model.
While reliable objective full-reference metrics are known
and have been studied multiple times, no-reference quality
prediction by computer algorithms is a much harder problem.
Subjectively, both full and no-reference methods are in use,
though might answer slightly different questions. Full reference methods measure “fidelity” — how close is the distorted
image to the reference — while no-reference methods rate the
overall “quality” of a presentation.
In the following, we will focus on full-reference objective quality indices. Here one can again distinguish between
display-referred and luminance-independent metrics. The former expect that the values in images or video correspond to the
absolute luminance emitted from a display on which a presentation is shown. The latter accept any relative radiance values
as input. They assume that human vision is approximately
scale-independent, a property that is equivalent to the “Weber’s
Law”.Generally, the objective metrics designed for SDR such
as PSNR and SSIM are ill-suited for HDR content. These
metrics take as input a gamma corrected image and consider
this content in an approximate perceptually uniform space.
However, this assumption is valid for CRT displays that are
working typically in low luminance range (0.1 to 80 nits). This
is not anymore valid for brighter displays. Here distortions
that are barely visible in CRT displays, will be noticeable.
A simple encoding of the physical luminance that makes

IEEE SPM MAGAZINE, VOL. 34, NO. 5, SEPTEMBER 2017

objective metrics for SDR content applicable for HDR content
is to transform luminance values into a perceptually uniform
(PU) space. Two major objective metrics for evaluating HDR
content directly have emerged lately [11], [12]. Both are fullreference human visual system based metrics. HDR-VDP2 is an objective metric capable of detecting differences in
achromatic images. For that, it includes a contrast sensitivity
model inspired by the properties of the human visual system
for a wide range of luminance values. The metric takes test
and reference HDR images as input which are then mapped
first to a perceptual space and frequency-filtered in several
orientation and frequency specific subbands modeling the first
layer of the visual cortex. In each subband, a masking model
is applied. The subband wise difference of the cortex-filtered
output is then used to predict both visibility as the probability
of defect-detection, and quality, as the perceived magnitude of
distortion.
The dynamic range independent (DRI) metrics attempt to
evaluate image quality independent of the dynamic range
of the two images to be compared. If the dynamic range
would be identical, the pixel-wise difference between test
and reference images would already provide an indicator of
the visible artifact to be measured. If the dynamic range is
different, though, a per-pixel difference could either be due to
an image defect degrading image quality, or due to the change
of the dynamic range. In the latter, the visible differences
in the test image should not be classified as visual artifacts.
To distinguish between the two causes, such metrics apply a
model of the HVS based on the detection and classification
of visible changes in the image structure. These structural
changes are a measure of contrast, and can be categorized
as follows[12]:
• loss of visible contrast, i.e., if a contrast is visible in the
reference image but is not anymore visible in the test
image. This happens, for example, if the tone mapper
compresses details so much such that they become invisible after tone mapping.
• amplification of invisible contrast, i.e., the opposite of
the above effect. This type of degradation is typical for
inverse tone mapping when, due to contrast stretching,
contouring artifacts start to appear.
• reversal of visible contrast, i.e., if the contrast in the test
image is the inverse of the contrast in the reference image.
Such defects appear, for example, due to clipping after
tone mapping.
The evaluation of HDR video content is also a very important issue in various applications and standardization activities.
Recently, the HDR-VQM metric has been proposed [11] to
provide a feasible objective metric to evaluate quality in HDR
video content. Video quality is computed based on a spatiotemporal analysis that relates to human eye fixation behavior
during video viewing.
IV. W HAT WE HAVE LEARNT
Based on this article, readers could have learned what HDR
imagery is, including all steps involved in its specific imaging
pipeline, and what extra features it is capable to provide to

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the end-user. In particular, HDR imagery is conveying to the
end-user an extraordinary experience, when compared to the
traditional digital imaging as known today, e.g., 8-10 bits. To
better understand improvements introduced by HDR content,
which is perceived by the end-user, one can compare it to
what happened about 50 years ago when television moved
from black/white to color.
V. ACKNOWLEDGMENTS
EPFL author acknowledge the Swiss State Secretariat for
Education, Research and Innovation (SERI), for the European
Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 643072
- QoE-Net.
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[Online]. Available: http://doi.acm.org/10.1145/1360612.1360668

Alessandro Artusi (artusialessandro4@gmail.com) is a researcher at KIOS
Research and Innovation Center of the University of Cyprus. He is a member
of the ISO/IEC/JCTC1/SC29/ WG1 Committee (also known as JPEG), one of
the editors of the JPEG XT standard, and recipient of the Emerging Standards
Awards from the British Standard Institute.

IEEE SPM MAGAZINE, VOL. 34, NO. 5, SEPTEMBER 2017

Thomas Richter (richter@tik.uni-stuttgart.de) is a researcher at the TIK
Computing Center of the University of Stuttgart. Member of SC29WG1 since
2003.

Touradj Ebrahimi (Touradj.Ebrahimi@epfl.ch) is a professor at Ecole Polytechnique Fédérale de Lausanne heading its Multimedia Signal Processing
group. He is also the convenor (chair) of the JPEG Standardization Committee.

Rafał K. Mantiuk ( rkm38@cam.ac.uk) is a senior lecturer at the Computer
Laboratory, University of Cambridge, United Kingdom. He is the author of
a popular HDR image quality metric, HDR-VDP-2; the coauthor of pfstools,
software for high-dynamic range image processing.

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