=Paper=
{{Paper
|id=Vol-2688/invited1
|storemode=property
|title=On Measures of Visual Contrast and Their Use in Image Processing (invited paper)
|pdfUrl=https://ceur-ws.org/Vol-2688/invited1.pdf
|volume=Vol-2688
|authors=Azeddine Beghdadi
|dblpUrl=https://dblp.org/rec/conf/cvcs/Beghdadi20
}}
==On Measures of Visual Contrast and Their Use in Image Processing (invited paper)==
https://ceur-ws.org/Vol-2688/invited1.pdf
On Measures of Visual Contrast and Their Use
in Image Processing
Azeddine Beghdadi
L2TI, Institut Galilée, Université Sorbonne Paris Nord, France
azeddine.beghdadi@univ-paris13.fr
Abstract. Visual contrast is one of the most studied notions in the
field of Visual Neuroscience and Psychophysics. It is a measure asso-
ciated with a psycho-physical sensation that is not easy to define in
an objective and unique way. Indeed, the contrast as defined through
Weber’s famous experiment is associated with the subjective notion of
just noticeable difference between a stimulus observed against an uni-
form background. In this paper a critical review of different representa-
tive measures of visual contrast and their uses in various applications is
presented. This study provides also some insights on how to define the
contrast and how to chose the most appropriate measure for developing
contrast based methods for visual information processing and analysis.
Perspectives and challenges that remain to be addressed are also dis-
cussed in light of new trends in visual information processing. Through
this study, it becomes clear that the concept of contrast and its use
are highly application-dependent and that there is no universal contrast
measure. It is also shown that, given the large number of psycho-physical
parameters involved, it is not easy to define a contrast measure that is
easy to use in the various methods of image processing and analysis.
Simplifying contrast models without neglecting the most fundamental
aspects seems to be the most pragmatic and practicable solution.
Keywords: Contrast Measures · Just Noticeable Difference (JND) · Vi-
sual Contrast · Image Processing and Analysis · Perceptual Image com-
pression · Perceptual watermarking.
1 Introduction
Visual contrast is one of the most studied notions in the field of Psychophysics
and Visual Neuroscience [13, 20]. It is a measure associated with a psycho-
physical sensation that is not easy to define objectively and in a unique way.
Indeed, contrast as defined through Weber’s famous experiment is associated
with the subjective notion of Just Noticeable Difference (JND) between a stim-
ulus observed against an uniformed background. From the study conducted by
Copyright © 2020 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0). Colour and Visual
Computing Symposium 2020, Gjøvik, Norway, September 16-17, 2020.
2 A. Beghdadi
Hecht [25] it seems that Bouguer (1760) was the first to study the differential
sensibility of the Human Visual System (HVS). The idea of measuring the just
noticeable increment needed to discern one stimulus from another was later stud-
ied by Weber and Fechner [13]. Indeed, the idea of analysing and measuring the
minimum increment of intensity between two stimuli in order to discern them
is at the basis of the very definition of what is commonly known as the Weber-
Fechner contrast [17]. In fact it was Fechner, a few years later, who formalized
Weber’s results in an experimental and theoretical framework. Weber-Fechner
law has led to many experiments and sophisticated models for understanding
the notion of perceptual contrast [25,33]. Since these pioneer works, many visual
contrast measures have been proposed in the literature [16, 24, 31, 35, 39, 41, 42].
The taking into account of psycho-physical factors such as perceptual con-
trast in a visual information processing and transmission system is essentially
linked to the fact that the observer is the key element in any chain of acquisition,
processing and transmission of visual information. Indeed, the human observer
is often the supreme judge in the evaluation of the different stages of the process-
ing and transmission chain. It is therefore quite natural to think of developing
methods inspired by the mechanisms of the human visual system to incorpo-
rate perceptual criteria that meet the requirements of the observer. It is worth
noticing that among the perceptual aspects of the HVS, visual contrast is one
of the widely investigated psycho-visual aspects in vision research [13,24,25,48].
According to this principle and reasoning several methods of image processing
and analysis based on contrast measure have been developed [3, 48]. Indeed,
contrast plays a prominent role in important applications such as medical imag-
ing [19], image quality enhancement (IQE) [6] and image quality assessment
(IQA) [14, 15], image fusion [9, 44], and other applications such as image quan-
tization and compression [12, 28]. In this article we will review some of these
applications and discuss the most suitable contrast measures in each case. This
is not an exhaustive study of all known methods, but we will limit ourselves
to a few representative studies. The main objectives of this contribution are as
follows:
– to discuss the fundamental criteria and factors that define visual contrast
and present a critical review of the most representative contrast measures
and associated models,
– provide a brief description and discussion on some applications in the field
of visual information processing based on perceptual contrast measure
– provide some insights on how to use and chose the appropriate contrast mea-
sure in some selected visual information processing and analysis applications,
The paper is organized as follows: first some historical contrast models are
discussed in Section 2 followed by a classification discussion of some represen-
tative contrast measures in Sections 3, 4, and 5. Section 6 is dedicated to some
selected contrast based applications. Finally the paper ends by concluding re-
marks, challenges and some future directions of research in Section 7.
On Measures of Visual Contrast and Their Use in Image Processing 3
2 Perceptual Contrast: History and Basic Notions
This section presents a brief historical review of the research on contrast mea-
sures and associated models developed by the scientific research community in
psycho-physics, optics, neuroscience and digital visual information processing.
The notion of visual contrast has been introduced in a clear and well defined
theoretical and experimental framework for the first time by Fechner [17]. Since
this pioneering work based on Weber experiments and model, several studies
have been carried out to enrich existing models with advances on both the theo-
retical and experimental levels [24, 25, 33, 35]. The introduction of the frequency
sensitivity aspect of contrast has been established through psycho-physical ex-
periments [13]. The directional selectivity of HVS has been clearly demonstrated
by Hubel and Wiesel [26], the two Nobel Prize laureates. Other aspects related to
the distance or viewing angle parameter have been introduced explicitly accord-
ing to optical models [33] or implicitly through a multi-resolution representation
of visual contrast [14, 44]. However, the colour aspect was neglected for a long
time in the first experiments. This is due to the fact that the notion of contrast
is much more related to the detection of details and more particularly the con-
tours of objects which is traditionally regarded as an achromatic process [13].
However, it has been shown that the colour aspect also plays a major role in
contrast sensitivity [22]. Other spatio-temporal and 3D aspects could be incor-
porated in the definition of the spatio-temporal contrast. It is worth noticing
that to the best of the author’s knowledge there is no measure of contrast in
an analytical form that integrates all these psycho-physical and geometrical pa-
rameters. Rather simplified and mathematically tractable expressions are often
used to define an objective measure of visual contrast. The essential criteria and
HVS properties that could be taken into account in the visual contrast measure
are given below.
– sensitivity to the relative change of luminance
– luminance adaptation phenomena
– frequency selectivity
– directional sensitivity
– multiscale/multiresolution aspects
– color aspects
– viewing distance (or viewing angle)
– temporal aspects (in the case of spatio-temporal visual signals)
Note that it is not easy to define a contrast measure that integrates all these
properties and criteria. Simplifications are often used, keeping only a few prop-
erties and aspects to establish a measure of visual contrast. There are two ways
to define contrast, depending on whether we associate a single value to the entire
image or a value for each pixel or group of pixels. In the first case it is global con-
trast, while in the second case it is local contrast. The contrast measure could be.
computed in the spatial domain, frequency domain, and even multi-resolution
or multi-scale representations.
4 A. Beghdadi
It should be noted that given the different forms of representation of the
visual signal, the large number of contrast measures proposed in the literature
and the different contexts and fields of application, it is not easy to classify
all the contrast measures. In the following we classify contrast measures into
three categories that is psycho-physic and neuroscience based contrasts, local
structure based contrasts and statistical information based contrasts. Here we
limit ourselves to a few representative contrast measures from each category.
3 Psycho-physic and neuroscience based contrasts
In this first category are some representative contrast measures based on psycho-
physical experiments or models from theoretical and experimental studies in
neuroscience. Figure 1 illustrates the simultaneous contrast phenomena. It also
illustrates one of the most relevant parameters that should be taken into account
in the contrast definition, that is the influence of the surround and background
luminance in the visual perception of stimuli. This figure represents the foveal
image model used in several psycho-physical experiments such as the Moon and
Spencer experiment [33].
Fig. 1. An example of imultaneous contrast experiment.
What should be observed in Figure 1 is the effect of the background on
the appearance of the small dark disc and the ring around it. Indeed, as can
be seen, despite having the same luminance and the same gradient in the five
configurations the disc and the ring appear different according to the position
in the non-uniform background. It can be concluded from this example that the
gradient alone cannot account for the visual appearance of the visual signals. The
background against which the object is observed is important. This observation
leads us to question the many contrast measures based solely on the luminance
difference between stimuli, i.e. gradient. This is one of the reasons why it is
important to define contrast as a measure of relative variation in luminance, i.e.
a relative ratio. This is the case with the contrast measure proposed by Weber
and Fechnner [17] described below.
On Measures of Visual Contrast and Their Use in Image Processing 5
3.1 Weber-Fechner contrast
Weber-Fechner contrast is one of the simplest contrast measures [17]. However,
it only applies to simple scenarios in which a uniform luminance background
L contains an object with an incremental luminance ∆L. The aim of Weber-
Fechner (W-F) contrast is to determine the value of ∆L, referred to as the
JND, which makes the object (target) just visible. The W-F contrast measure
is defined by:
∆L
CW = (1)
L
One of the most important results of the experiments conducted by Weber and
Fechner is that this ratio remains constant over a fairly wide range of luminance
values. This value is called the Just Noticeable Contrast (JNC) and is of the
order of .02. It should be noted that many sophisticated contrast measures, used
in visual information processing and coding, are in one form or another based
on the Weber-Fechner definition [2, 14, 35, 42, 44, 50].
3.2 Michelson contrast
The Michelson contrast was first introduced in a purely physical context and
concerns the measurement of the visibility of interference fringes produced by
thin films [32]. However, it has been widely used in psycho-physical experiments
and more particularly the study of the frequency sensitivity of the HVS to per-
ceptual contrast [13, 45]. The Michelson contrast is defined as follows:
Lmax − Lmin
CM = , (2)
Lmax + Lmin
where Lmin and Lmax correspond to the minimum and maximum luminance
values in the optical image, respectively. Although this contrast has been used
extensively by the scientific community of vision research, it has several limita-
tions. Indeed, its use in the case of natural images can lead to over- or under-
estimation of contrast and in particular in the case of images contaminated by
impulse-type noise. It is also the case of images containing some singularities,
or isolated points even if they are perceptually invisible. Moreover, it does not
take into account the influence of the background and in particular the lumn-
inance adaptation phenomenon. It also does not consider the frequency aspect
although the stimulus signal shape is designed from a sinsusoidal function. It is
nevertheless surprising that such a simple contrast model has been widely used
by the vision research community for almost a century..
3.3 Moon-Spencer contrast
The main idea behind Moon and Spencer’s [33] model is to apply Holladay’s prin-
ciple [33] that any non-uniform background may be replaced by another uniform
luminance that produces the same perceptual effect. This leads to the definition
6 A. Beghdadi
of luminance adaptation that can be calculated according to a simplified model.
Based on this principle, Moon and Spencer proposed a simple enough model to
express the adaptive luminance, which is given below.
LA = αS LS + αB LB , (3)
Where LS and LB are the luminance of the surround (immediate neighborhood)
and that of the background (or far surrounding), respectively. The two weight-
ing parameters, αS and αB are set experimentally to the values .923 and .077,
respectively. Moon and Spencer define the minimum perceptible contrast as:
√
CW A + LA 2 if LA ≥ LS
LS
Cmin = C q
2
2 . (4)
W A + LS if LA < L S
LS LA
Where CW corresponds to the Weber-Fechner JNC contrast, and A is a constant
determined experimentally from psycho-visual tests and Hecht’s law [25] which
is equal to 0.808. Note that this contrast is more interesting in the sense that
it corresponds more or less to realistic configurations. It has been successfully
used and adapted to digital images in various applications [6, 27, 28, 34].
3.4 Lillesaeter contrast
Noting the asymmetry in Weber’s definition of contrast, Lillesaeter proposed two
measures of contrast [30]. In the first one only luminance is taken into account
while in the second definition he proposes to include the geometry and shapes
of the objects observed in the image. Indeed, it is observed that the negative
and positive Weber-Fechner contrasts with the same absolute increment are not
perceived equally. The Lillesaeter contrast is then defined as
LO
C = log (5)
LB
where LO and LB correspond to the average luminance of the object and the
background, respectively. Note that when LO and LB are very close to each
other the Lillesaeter contrast is equivalent to that of the Weber-Fechner contrast
measure
LO − LB
C= ≈ log LO − log LB (if |C| << 1). (6)
LB
The second definition of Lillesaeter contrast incorporates the object contour
geometry as perceived by human. The idea of taking into account the geometry
of perceived objects is relevant but impractical in the evaluation of contrast in
digital images. Indeed, it leads to the computation of curvilinear integral which
requires the exact knowledge of the object contours. This leads inevitably to an
ill-posed problem which is image segmentation.
On Measures of Visual Contrast and Their Use in Image Processing 7
3.5 DOG based contrast
Based on models describing the retinal ganglion cells and Lateral Geniculate
Nucleus (LGN) responses [29] to visual stimuli on the other, Tadmor and Tol-
hurst [42] proposed three measures of local contrast. The principle is based on
the use of linear filtering by two isotropic Gaussian Impulse Responses (IR) of
different sized kernels. The bandpass behaviour of the HVS is then modelled
through the Difference Of Gaussian (DOG) model [29]. The two Gaussian IRs
are associated with two regions, a center zone Ωc and a surround zone Ωs , to
mimic the receptive fields of the ON and OFF cells [20, 29].
The two responses are given by:
Iσc (i, j) = (I ∗ hσc )(i, j), (7)
and
Iσs (i, j) = (I ∗ hσs )(i, j) (8)
where I is the input image signal, hσc and hσc are the two Gaussian IR associated
with the center and surround zones .
The convolutions are performed in the sliding windows Ωc (i, j) and Ωs (i, j) of
odd size [−3σc , +3σc ]x[−3σc , +3σc ] and [−3σs , +3σs ]x[−3σs , +3σs ], respectively.
Three local contrasts are then defined as follows:
Iσc (i, j) − Iσs (i, j)
C1DOG (i, j) = , (9)
Iσc (i, j)
Iσc (i, j) − Iσs (i, j)
C2DOG (i, j) = , (10)
Iσs (i, j)
Iσc (i, j) − Iσs (i, j)
C3DOG (i, j) = , (11)
Iσc (i, j) + Iσs (i, j)
The global contrast is derived by averaging the local contrasts. It can be noticed
that these contrasts do not take into account the directional and frequency se-
lectivity nor the colorfulness aspects. Note that this contrast expressed as a ratio
between a differential signal component and a low-pass component is somehow
inspired by Weber-Fechner’s simple model and corresponds well to the notion of
visual contrast.
4 Local structure based contrast measures
In this section, we introduce and discuss some representative contrast measures
that could be used in digital image processing and analysis applications. These
are essentially measures that explicitly or implicitly incorporate some features
of the SVH.
8 A. Beghdadi
4.1 Edginess based contrast measure
It is well established that one of the primitives of the image signal that is most
related to contrast and therefore to the visibility of detail is contour informa-
tion. Indeed, the most representative contours of the image signal correspond to
the spatial frequencies where the CSF reaches its maximum values [13, 29, 45].
Inspired by the contrast defined by Gordon and Rangayan in their contrast en-
hancement method [19], Beghdadi and Le Negrate introduced a new measure of
contrast incorporating edginess information [4]. This local contrast measure is
computed using a sliding window of odd size . For each Wij window, the mean
edge gray-level at the center pixel (i, j), is computed as
P
Φ(∆kl )f (k, l)
(k,l)∈Wij
E(i, j) = P . (12)
Φ(∆kl )
(k,l)∈Wij
In (12), f (k, l) corresponds to the gray level at the pixel (k, l) and Φ(∆kl ) is
an increasing monotonic function of the gradient operator at (k, l). A simple
function would be ∆nkl , with n > 0. The local contrast is expressed as:
|E(i, j) − f (i, j)|
C(i, j) = . (13)
E(i, j) + f (i, j)
Note that this contrast measure does not take into account the frequency selec-
tivity nor the directional selectivity aspects. Furthermore, when used in CE
it may introduce halo effects around the edges which is the result of over-
enhancement [5, 38]
4.2 Multiresolution contrasts
Toet was the first to propose a contrast measures taking into account the multi-
resolution aspect for image fusion [44]. It is based on the Burt and Adelson
pyramid decomposition scheme [10]. The contrast is expressed as
gk (i, j) − gk−1 (i, j)
Ck (i, j) = , (14)
gk (i, j)
Where the components gk (i, j) and gk−1 (i, j) are the gray-level of pixel (i, j) in
the Gaussian pyramid at the k th and (k − 1)th levels, respectively. Note that this
expression of local contrast is also inspired by Weber-Frechner’s intuitive defini-
tion. Indeed, the numerator is nothing more than a differential signal (intensity
increment) and the denominator is the signal against which the increment is
measured (reference signal). Here is also,the directional sensitivity and the col-
orfulness aspects are note taken into account. This contrast measure has inspired
several works and led to various interesting applications such as image fusion [44],
contrast enhancement [40] and image distortion prediction [14].
On Measures of Visual Contrast and Their Use in Image Processing 9
4.3 Bandlimited contrast
By exploiting the results of psycho-physical experiments on the frequency sen-
sitivity of the HVS to contrast and in particular its behaviour as a bandpass
filter, Peli introduced the notion of band-limited contrast [35]. The image signal
is analyzed using a bank of cos-log type isotropic band-pass filters to extract
the different components describing the signal at different frequency bands. The
image component captured by the k th channel is given by
gk (i, j) = (f ∗ hk )(i, j). (15)
Where hk , is the impulse response corresponding to the k th band-pass filter and
gk the associated filtered component. For each pixel (i, j) in the k th component,
the contrast is expressed as:
gk (i, j)
Ck (i, j) = (16)
bk (i, j)
where the bk is given by:
k−1
X
bk (i, j) = gm (i, j). (17)
m=0
Here, too, it can be assumed that this measure is somehow inspired by the
intuitive idea of Weber and Fechner’s model. Indeed, the numerator is the differ-
ential signal , i.e. pass-band signal, and the denominator contains the sum of all
the lower frequency components, i.e. baseband signal. This ratio does measure a
relative change in signal amplitude just as in the Weber-Fechner contrast model.
Like Toet’s contrast, Peli’s contrast can be used for complex natural images,
unlike other contrasts such as Weber or Michelson. However, the lack of direc-
tional selectivity and colorfulness aspects of Peli’s contrast limits its use in some
real-world applications where these aspects play prominent roles.
4.4 Daly contrast
The contrast model proposed by Daly [14] is essentially based on the cortex trans-
form introduced by Watson [1]. It should be noted that in this contrast model
both frequency selectivity and directional selectivity are taken into account by
means of two families of linear filters. The input signal f is first analysed by
means of two cascades of isotropic band-pass linear filters called ”dom” and
”fan” corresponding to the frequency selectivity for the former and the direc-
tional selectivity for the latter [14]. The filtered versions of the input signal f
are given by:
gkl (i, j) = (hkl ∗ f )(i, j) (18)
where k and l are the dom and fan filter indices as defined in [1]. Daly contrast
is then defined by
10 A. Beghdadi
gkl (i, j) − g kl (i, j)
Ckl (i, j) = . (19)
g kl (i, j)
Where g kl is the mean of the band (k, l). Note that this measure is unstable
because of the denominator, which tends to zero. Daly proposes two solutions
to overcome this problem. He then introduces two contrast measures where the
denominator is replaced in one case by the baseband signal mean and in the other
by the baseband signal calculated at each pixel. These two modified contrast
measures are given by:
gkl (i, j) − g kl (i, j)
Ckl (i, j) = . (20)
gK
and
gkl (i, j) − g kl (i, j)
Ckl (i, j) = . (21)
g K (i, j)
Note that Daly contrast model does not incorporate the colorfulness aspect. This
contrast measure has been used successfully in the design of image distortion
prediction models [14].
4.5 Isotropic contrast
The consideration of the multi-scale aspect in the visual contrast measure is
to some extent related to the characteristics of the HVS [26]. One of the first
contrast measures based on wavelet analysis was introduced by Winkler and
Vandergheynst [50]. It is surprising that most of the contrast measures proposed
so far did not explicitly take into account the multiscale aspect. However, it
should be noted that Toet’s proposed measure is somehow quite close in that it
introduces the multi-resolution aspect. The main idea of the measure proposed
by Winkler and Vandergheynst was to overcome the limitations of the contrast
proposed by Peli. They proposed a contrast measure using a directional wavelet
decomposition based on a translation invariant multiresolution representation
using 2-D analytical filters. By combining the different analytic oriented filter
responses they derived the isotropic contrast expressed as follows.
r P
2 |gkl (i, j)|2
l
Ck (i, j) = , (22)
gk (i, j)
where gkl (i, j) is the gray-level at pixel (i, j) in the band-limited directional
filtered image obtained by filtering the input signal f by the directional wavelet
at resolution k and direction l. Similarly to Peli’s contrast, the denominator cor-
responds to the baseband signal, i.e. the filtered signal with the scaling function
at scale k. It has been demonstrated that in contrast to the Peli’s model, this
new contrast gives a flat response to sinusoidal patterns [50]. However, it is im-
portant to note that although in its design this contrast uses directional filters,
On Measures of Visual Contrast and Their Use in Image Processing 11
it provides an isotropic contrast measure. This could be beneficial for certain
applications where directionality is not important, such as in the case of digital
watermarking [46].
4.6 Directional bandlimited contrast
Based on the work of Peli and Daly, Dauphin et al. [16] proposed a contrast
where directional selectivity is taken into account in the final contrast measure.
Indeed, other contrasts such as those of Daly and Winkler-Vandergheynst inte-
grate directional selectivity in the analysis of signal components but the final
contrast is rather isotropic. Whereas, in the model defined in [16] the final con-
trast measure is anisotropic in the sense that the final response emphasizes the
most directional salient signal components in the signal. A non-linear operation
of type max is thus used for the calculation of the local contrast. The image sig-
nal is analysed using a multichannel Gabor decomposition. The local directional
bandlimited contrast is computed as
max (|gkl (i, j)|)
l
Ck (i, j) = (23)
gk (i, j)
Where gk (i, j) is the gray-level associated to the frequency sub-band ρk and to
one of the four directions (0, π/4, π/2, 3π/4) represented by l. The normaliza-
tion term gk (i, j) represents the total energy of the background below the band
(k) which is obtained by filtering the original image by a Gaussian filter with a
standard deviation
σk = 0.75ρk (24)
This local bandlimited directional contrast does not incorporate the colorfulness
aspect. It has been compared to Peli’s contrast and has been proven more efficient
and less complex than the wavelet-based contrast proposed in [50].
4.7 Multiscale color contrast
The RAMMG contrast measure proposed by Rizzi et al. [39] is one of the few
measures of contrast that incorporates both multi-resolution and colour aspects.
The image signal is decomposed using a pyramidal scheme in the CIELAB colour
space. At each level of resolution each pixel is associated with a local contrast
defined as the response of a pseudo-Laplacian computed by convolving the input
signal I by the mask S given by:
√ √
2 2
1
1 2 2
S= √ 1 0 1 . (25)
4 + 2 2 √2 √2
2 1 2
The local contrast at the k th level of the pyramid is computed as follows.
Ck (i, j) = (Dk ∗ S)(i, j), (26)
12 A. Beghdadi
where Dk (i, j) is the absolute difference of the luminance between the current
pixel (i, j) and the central pixel at the k th level of resolution. The RAMMG global
contrast is obtained by averaging all local contrasts across all the different levels
of the pyramidal decomposition.
X Xl XK l W H
1
C RAM M G = Ck (i, j). (27)
Wl × Hl × K i=1 j=1
k=1
where K is the total number of decomposition levels and Wk and Hk represent
the width and height of the image at the k th level respectively. A very similar
global contrast measure called Gobal Contrast Factor (GCF) has been proposed
in [31]. GCF contrast has been found somehow consistent with subjective ranking
of a relatively wide range of natural images with varying contrast. However, it
should be noted that both contrast RAMMG et GFC are not relative measures of
the variation in the energy of the image signal and therefore cannot be included
in the family of contrasts conforming to the notion of contrast as defined by
Weber-Fechner.
5 Statistical features based contrasts
Very few contrasts measures based on statistical information have been intro-
duced in the literature. These measures are often related to the pixel values
distribution, such as the grey-level histogram or the 2D distribution computed
from the grey-level cooccurrence matrix (GLCM). Here we limit ourselves to
three contrast metrics based on some simple statistical features of pixel values.
5.1 Texture contrast measure
Haralick was the first who introduced the idea of using some statistical invariant
features for texture analysis. The set of these spatial descriptors introduced
in [23] are based on the GLCM computed from the digital image. Among the
Haralick’s texture descriptors a global contrast is defined. It is computed as
follows.
K−1
X K−1
X
CH = )(i − j)2 pij (28)
i=0 j=0
Where i and j are the grey-levels of adjacent pixels in a defined neighbourhood
and pij is the joint mass probability function computed from the GLCM. Al-
though this measure has always been identified as a contrast, it does not meet
the basic criteria to be truly considered as a measure of contrast in the usual
sense and in line with psycho-physical experiences and the notion of contrast
that has been well established since the 19th century.
On Measures of Visual Contrast and Their Use in Image Processing 13
5.2 Mutual information based contrast measure
Another way to exploit inter-pixel correlation, directly related to image contrast,
is to consider the measure of mutual information extracted from the GLCM. A
new global contrast measure based on mutual information has thus been intro-
duced for the first time to quantify the side effects such as saturation or halo
effect that could result from contrast enhancement [38]. This contrast measure
is defined by:
K−1
!
X K−1X pxy (i, j)
CM I = pxy (i, j) log2 , (29)
i=0 j=0
px (i)py (j)
where pxy is the joint probability mass function of the gray-level, whereas px and
py represent the marginal probabilities computed from the GLCM. While this
contrast is simple to compute, however, it does not provide information directly
related to visual contrast as it is purely based on statistical analysis of the signal
values distribution.
5.3 Root Mean Square contrast
The Root Mean Square (RMS) of the luminance in natural images has been
considered by Bex and Makhous [7] as a potential contrast measure in their
study on human observer sensitivity to contrast. As noticed by these authors,
this measure when divided by the average mean luminance of the image is a
good predictor of the relative contrast.
Another version of this RMS based contrast has been proposed by Frasor
and Geisler [18] to make it more suitable to natural images . Local contrast
is measured in different randomly selected patches in the image. The contrast
associated with a given patch is calculated as follows:
v
u N
u 1 X (Li − L)2
CRM S = t wi . (30)
wN i=1 L2
N
X
wN = wi (31)
i=1
where N is the total number of pixels in the patch, Li is the luminance of the ith
pixel, L the patch luminance and wi is a windowed isotropic weighting function
given by:
π
wi = 0.5 × cos ri (32)
p
p
where, ri = (xi − xc )2 + (yi − yc )2 , p is the radius of the patches , (xi , yi ) is
the position of the ith pixel within the patch, and (xc , yc ) is the center of the
patch. The patch luminance is given by
N
1 X
L= w i Li (33)
wN i=1
14 A. Beghdadi
It could be noticed that none of these statistical information based contrast
measures take into account the spatial frequency and directional content of the
visual signal. Nor do they incorporate other important aspects such as the lumi-
nance influence of near and far surrounds, the viewing distance and chromatic
aspect. Table 1 summarizes the key features of these representative local and
global contrast measures.
Concluding remarks From this brief review, we conclude that it is uneasy
to find a simple contrast measure that incorporates all the relevant perceptual
aspects related to the contrast notion. Therefore, most often simple measures
derived from psycho-physical experiments are used and adapted to digital im-
ages to solve a number of real problems such as image and video processing,
analysis and compression. Despite the enormous amount of work dedicated to
visual contrast, it is still not easy to evaluate and compare the different contrast
measures objectively. However, a few studies limited to subjective evaluation
have been carried out in the literature [41].
Moreover, through this critical analysis on the existing contrast measures, it
becomes important to answer some relevant questions. One important question
is: what are the most relevant visual signal characteristics that the contrast mea-
sure should capture? To the best of author’s knowledge none of the published
work addressed this issue properly. However, some interesting studies have been
dedicated to address this critical question [36]. Peli conducted a thorough ex-
perimental study in 1997 on how to define the contrast measure [36]. To be
consistent with the findings on the human visual perception the study provides
guidelines confirming that computational contrast metrics should take into ac-
count multiscale aspects [26].
It is also worth noticing that there is no clear and objective criteria nor
ground truth data to exploit in comparing the proposed contrast measures. Al-
though, some attempts have been made in the study of different measures of
contrast in digital images [41].
6 A Brief Overview of the use of Contrast in VIPA
An important issue to consider is how the contrast measure is defined and used
in developing various methods for Visual Information Processing and Analysis
(VIPA). We limit ourselves here to a few applications where the notion of con-
trast plays a predominant role. It should be noted that the choice of contrast
measure depends on the application. It is not always easy to make an appropriate
choice and pragmatic solutions are often used, based mainly on feedback.
6.1 Image Quality Assessment and Enhancement
Most objective HVS-based image quality methods explicitly or implicitly incor-
porate contrast measure [11]. Peli’s contrast and its variants have been success-
fully incorporated in the design of IQA measures [15] and distortion predictor
On Measures of Visual Contrast and Their Use in Image Processing 15
Table 1. Summary of some representative contrast measures.
Contrast Year Key features
Weber-Fechner contrast [17] 1860 Defined on simple images (an object of uniform
intensity in a uniform background). Could not be
applied to natural and complex images
Michelson contrast [32] 1927 used for sinusoidal signals, not adapted for natural
images. Does not integrate any HVS properties.
Moon-Spencer contrast [33] 1944 considers object embedded in a non-uniform lu-
minance background. Can be applied to digital
images of natural scenes with a few adjustments
and adaptations.
Haralick contrast [23] 1973 capture the average local variations and spatial
dependence of the pixels computed from gray-level
co-occurrence matrix
Edginess based contrast [4] 1986 uses local edginess information in an image and
quantifies local sharpness of the contour by mea-
suring the visibility of the salient features by using
a sliding window.
Multiresolution contrast [44] 1989 a spatial multi-resolution contrast based on Gaus-
sian pyramid decomposition.
Bandlimited contrast [35] 1990 An image is decomposed into several channels
using a bank of cosine-log bandpass filters, well
suited for complex images and often used in the
computation of the quality of encoded images.
Daly contrast [14] 1992 It is band-limited contrast based on the cortex
transform and used by Daly in the Visible Differ-
ence Predictor (VDP) model [48].
Lillesaeter contrast [30] 1993 not easy to use in practice since it requires be-
forehand knowledge of the object contours and
computation of the curvilinear integral along the
boundaries of the objects contained in the ob-
served image.
Winkler-Vandergheynst 1999 uses non-seperable directional filters. It has the
contrast [50] advantage of giving a flat response instead of an
oscillating response to sinusoid gratings.
DOG based contrast [42] 2000 Based on Lateral Geniculate Nucleus responses
model [20]. It uses Difference Of Gaussian (DOG)
to model the bandlimited responses.
Directional contrast [16] 2003 It is local directional bandlimited contrast com-
puted from multichannel Gabor decomposition
and a nonlinear operation.
RAMMG contrast [39] 2004 Multilevel analysis (pyramid representation), 8-
neighborhood pyramid subsampling of the image
to various levels in the CIELAB.
RMS Contrast [18] 2006 a based on the RMS contrast introduced by
Bex and Makous [7] using a windowed isotropic
weighting function. It is a single scale contrast
based on only pixel values.
MI based contrast [38] 2015 based on the mutual information computed from
the gray-level co-occurrence matrix.
16 A. Beghdadi
models [14]. Another application where the concept of contrast is important con-
cerns the improvement of image quality and in particular Contrast Enhancement
(CE). The CE methods could be roughly classified into two categories, namely
direct methods and indirect methods [4]. The main idea of the direct methods is
to estimate the local contrast and to amplify it by means of a monotonic trans-
formation and to deduce the intensity of the pixel corresponding to this new
contrast. The contrast defined by Beghdadi and Le Négrate [4] and its variants
have been successfully used in many CE methods [3]. The edginess based contrast
measure has been also extended to stereo images by incorporating the depth in-
formation [21]. Another direct method based on the bandlimited contrast [35]
proposed by Tan et al. [43] which operates in the discrete cosine transform do-
main. Unfortunately most CE methods suffer from some side effects and there
is no unified framework to control these effects. Indeed, this pre-processing that
aims at amplifying the visibility of details by increasing gradient and sharpness
may introduce some artefacts such noise amplification, saturation and halo effect.
It is therefore useful to quantify these undesirable side effects. Many objective
measures for Contrast Enhancement Evaluation (CEE) based on the local or
global contrast have been studied in [37]. A critical study of CEE metrics has
revealed that the mutual information contrast measure is the most promising in
terms of simplicity and efficacy [5]. However, this metric has limitations and it
is in our interest to develop other measures that integrate the multi-scale and
multi-directional aspects. From this point of view the three contrast measures
defined in [8, 16, 50] are good candidates.
6.2 Visual Data Protection and Compression
The protection and coding of visual data are two classic problems of very active
research. Here, it is more precisely about image watermarking using perceptual
approaches [3]. The aim is to insert a watermark that is both invisible and re-
sistant to various attacks. This is then a very difficult problem where one tries
to achieve the best compromise between two antagonistic criteria that are ro-
bustness and transparency. Indeed, robustness requires putting more energy into
the watermark, which inevitably makes it visible and therefore breaks the trans-
parency. The visibility of the watermark is very much linked to the notion of JND
defined in the contrast measure. In this type of application where one seeks to
insert in a robust and transparent way the watermark the multi-scale or pyrami-
dal approach through JND measures related to the contrasts defined in [44,50] is
the most promising solution as shown by the studies in [34,46]. The other appli-
cation where contrast plays an important role is image compression with quality
control. This involves using contrast as a measure of the visibility of distortion
and artifacts inherent in lossy compression methods. The JND measure, again
very much related to contrast, and the visual masking phenomenon are the most
important parameters to be considered in the quantization and coefficient selec-
tion scheme in the transformed domain. The idea of exploiting contrast in the
quantifization of the image signal dates back to the work of Kretz [28] based on
On Measures of Visual Contrast and Their Use in Image Processing 17
the contrast of Moon and Spencer [33]. Since this pioneering work other more so-
phisticated contrast measurements have been successfully introduced into image
compression and video coding models [12, 47]. Another interesting perceptual
coding scheme proposed in [51] based on the Watson-Solomon Contrast Gain
Control (CGC) [49] model has been proposed for High Efficiency Video Coding
(HEVC). It is worth noticing that most of visually lossless coding methods in the
literature exploit the contrast measure in an explicit or implicit manner [3, 48].
The only contrast model for perceptual coding and quantization that seems to
be complete and efficient is the one introduced recently in [51]. Therefore, we
recommend to use this model for perceptual coding.
6.3 Image fusion
Image fusion is becoming an active field of research and especially with the reviv-
ing of artificial intelligence based approaches. The use of perceptual information,
and particularly contrast, in visual data fusion schemes seems to be the most
promising approach in various applications [8, 40]. There are several ways to
merge information, depending on the application and the data analysis method
used. A first approach is to exploit the multi-scale representation of visual in-
formation in the development of the fusion scheme. Perceptual contrast is one
of the signal features that could be used in the design of the fusion scheme. The
idea behind the use of perceptual contrast is to exploit the most relevant per-
ceptual information that is contained in the contrast map. The strategy consists
then in using the contrast measure in the weighting function used in the fusion
scheme. One of the attractive image fusion approach based on this idea is to
use the directional wavelet based contrast as done in [8]. This strategy could be
used not only in multimodal medical imaging but also in various applications
such multi-modal video-surveillance, multi-focus based computing photography
and hyperspectral imaging, to name a few. It is also the case of the contrast
enhancement method based on a perceptual fusion scheme proposed in [40].
7 Conclusions and challenges
Through this panoramic and chronological study on visual contrast, and the
underlying models and experimental studies, it becomes clear that it is not easy
to express a contrast measure where all the relevant psycho-physical factors and
parameters related to the notion of visual contrast are taken into account. Note
also that the notion of contrast and its use are very application-dependent and
the best way to exploit a visual contrast model to solve real problems is to
simplify it while keeping the most fundamental aspects.
Furthermore, it is difficult to classify the existing definitions and measures
related to the notion of contrast. This is mainly due to the various forms and
representations of visual and optical signals. Indeed, development of imaging
technologies has led to various image modalities. Therefore, it is now necessary
18 A. Beghdadi
to rethink and define the concept of contrast according to the modality of the
visual signal under consideration.
It should also be noted that the absence of a universal definition of visual
contrast has opened the door to formulations and extensions of this concept
to other contexts where one seeks only to quantify the difference between two
stimuli or elements of the signal. This has led, for example, to the consideration
of signal gradient measure as contrast in several studies. A unifying framework
with clear criteria for defining the contrast will certainly help in avoiding such
confusions and mistakes and allows to progress properly in exploiting vision
research results in developing efficient contrast based VIPA methods.
We can also see through this study that the measure of perceptual contrast
in the case of colour images is not sufficiently studied. According to the author’s
knowledge at the present time there is no well established definition or measure
of chromatic contrast that is recognized by the scientific community in the field
of vision research or digital image processing.
The temporal aspect is also important to introduce in the contrast measure.
Another aspect that could be taken into account is the inter-channel interactions
in the definition of multi-scale contrast. With the renewed interest in artificial
intelligence approaches for solving complex problems and in particular feature-
based learning approaches, the contrast may play a key role in the design of
perceptual loss functions in convolutional neural network architectures.
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