Quality Assessment of JPEG-distorted Face Images: Influence of
Affective Content
Silvia Corchs, Gianluigi Ciocca and Francesca Gasparini
Department of Informatics, Systems and Comunication, University of Milano Bicocca, Milano 20126, Italy
Keywords:
Image Quality Assessment, No Reference Metrics, Affective Content, JPEG-distortion.
Abstract:
In this work we investigate if the affective content of images influences the perception of image quality. Two
database are generated and psychophysical experiments are conducted, where participants rate the stimuli in
a five point Likert scale. We have fixed the semantic content, choosing only close-ups of face images, two
emotion categories (happy and sad images) and JPEG-distortion. Also the influence of the background is
considered. From the analysis of the subjective data we observe that the influence of affective content is more
evident for images of very high or very low quality. The subjective scores are further used as ground-truth
labels to train a five quality-class classifier. Two different feature spaces are used (visual features and quality
metrics) to train a SVM classifier.
1 INTRODUCTION
Objective image quality assessment is mainly related
to measuring the presence of distortions. Humans,
while scoring the quality of images, are not always
able to disregard all the factors related not only to the
distortion presence but also to other aspects like com-
plexity (Ciocca et al., 2017), image semantic (Siahaan
et al., 2018; Triantaphillidou et al., 2007) and affec-
tive image content (Van Der Linde and Doe, 2012).
When these different aspects concur to generate fi-
nal subjective rates, the objective metrics, that mea-
sure only distortions, may not properly predict human
judgments.
In particular, Image Quality Assessment (IQA)
studies do not in general take into account how does
the affective image content influence the quality per-
ception process itself. Most available IQA databases
are built starting from a set of pristine images which
are subsequently corrupted by introducing graded
simulated distortions (Sheikh et al., 2006; Larson and
Chandler, 2010; Corchs et al., 2014). In general,
such pristine images belong to different image content
classes such as indoor, outdoor, landscape, close-up,
etc., without considering the affective content dimen-
sion. On the other hand, recently a different type of
IQA database has been presented that contains sev-
eral authentic distortions on a very large number of
images (Ghadiyaram and Bovik, 2016).
Nowadays Internet of Things and wearable com-
puting is an active field of research and several smart
devices are able to capture emotional reactions to au-
dio visual stimuli. Within this field, knowing if the
quality of the signal influences its affective perception
can give insights in human computer interactions.
Up to our knowledge, only van der Linde and Doe
(Van Der Linde and Doe, 2012) addressed the affec-
tive dimension issue, analyzing the influence of affec-
tive image content on subjective quality assessment.
The authors set up a psycho-physical experiment and
found that participants were unable to disentangle af-
fective image content from objective image quality in
a standard IQA procedure.
Since the semantic content of an image can in-
fluence in different ways users perception of quality
(Siahaan et al., 2018), we here propose to fix the se-
mantic content, focusing on images depicting close
up of faces. Consequently, with respect to the af-
fective content, we consider two emotion categories:
happy versus sad images. Such choice is supported
by the experimental results of van der Linde and Doe
(Van Der Linde and Doe, 2012) who observed that
the pleasantness of the viewed image is a factor that
influences subjective rating of image quality. More-
over, users should be more affected by the emotions
elicited by the face expressions. With respect to im-
age distortions, as initial investigation, here we focus
on JPEG compression artifacts.
Besides the analysis of the affective dimension on
IQA, in this work we will also investigate the role of
386
Corchs, S., Ciocca, G. and Gasparini, F.
Quality Assessment of JPEG-distorted Face Images: Influence of Affective Content.
DOI: 10.5220/0006853403860393
In Proceedings of the 15th International Joint Conference on e-Business and Telecommunications (ICETE 2018) - Volume 1: DCNET, ICE-B, OPTICS, SIGMAP and WINSYS, pages 386-393
ISBN: 978-989-758-319-3
Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
the background when judging image quality. We set
up a psychophysical IQA experiment as a single cat-
egorical stimulus with five anchors (excellent, very
good, good, fair and poor quality). We also explore
different classification methods to predict these five
quality classes. Therefore, a classification approach
to the IQA problem is proposed and investigated in-
stead of the traditional regression analysis.
In Section 2 we present the two database gener-
ated in our Multi Media Signal Processing (MMSP)
laboratory to study the influence of both background
and affective content on IQA. In section 3 the psy-
chophysical experiments are described and the col-
lected data is analyzed (statistics, etc). In Section 4
the two different sets of objective features (visual fea-
tures and No Reference quality metrics) are listed and
the classification results are presented in Section 5.
2 THE MMSP-FACE DATABASE
The MMSP-Face database consists of 46 high quality
pristine images depicting close-ups of happy (23) and
sad (23) face images. In Figure 1 the corresponding
thumbnails are shown (all images are under Creative
Commons license). Starting from this database we
have generated:
The MMSP-FaceA database consisting of 230
images. Each of the pristine images of the MMSP-
Face database were JPEG distorted using the MAT-
LAB imwrite function with the following q-factors:
10, 15, 20, 30.
The MMSP-FaceB database. Starting from the
MMSP-FaceA database we have cut out the back-
ground and left only the face in each of the images.
The images are then distorted as in MMSP-FaceA.
This database also contains 230 images.
In Figure 2 an example image in its most distorted
version is shown for MMSP-FaceA and MMSP-
FaceB database. The datasets will be available online
at www.mmsp.unimib.it
3 EXPERIMENTS
Two different experiments were conducted. The first
one on the MMSP-FaceA database, and the second
one on the MMSP-FaceB database. Observers were
asked to judge the quality of each image of the
database in a single stimulus presentation through a
web-interface. They were explicitly instructed not
to judge the emotional nor the affective content of
the image. Before the start of the experiment, a
grayscale chart was shown to allow the observers to
Figure 1: Thumbnails of pristine images belonging to
MMSP-Face database.
Figure 2: An example image from MMSP-FaceA and
MMSP-FaceB.
calibrate the brightness and the contrast of the moni-
tor. After the calibration, six Ishihara tables were pre-
sented to the observers to estimate color vision de-
ficiency. If the participants did not report correctly
any of the six they were subsequently discarded from
the subjects’ pool. The stimuli were shown in ran-
dom order, different for each subject. Participants
rated the image quality in five discrete scales, rang-
ing from one star to five stars, corresponding to: ter-
rible (one star), not good (two stars), average (three
stars), very good (four stars) and excellent quality
(five stars). A web-interface was used in the exper-
iment, where stimuli were presented for an unlimited
time, up to response submission. In order to get the
Quality Assessment of JPEG-distorted Face Images: Influence of Affective Content
387
observers accustomed to the experiment, seven prac-
tice trials were presented at the beginning of each ex-
periment. The corresponding data were discarded and
not considered for any further analysis. To avoid fa-
tigue effects, the stimuli were divided in two subsets
of 115 images each. Therefore, two sessions of ex-
periments were performed for each of the considered
database. Participants were recruited from the Infor-
matics Department of the University of Milano Bic-
occa and were either students or researchers. None
of the participants failed the Ishihara test. All the
experiments reported in this article were conducted
in accordance with the Declaration of Helsinki and
the local guidelines of the University of Milano Bic-
occa (Italy). In the first experiment (MMSP-FaceA
database), 23 observers participated in the first ses-
sion, and 17 in the second session for a total number
of ratings collected of 115 × 23 + 115 × 17 = 4, 600.
In the second experiment (MMSP-FaceB database),
21 observers participated in the first session, and 19
in the second session for a total number of ratings col-
lected of 115 × 21 + 115 × 19 = 4, 600.
For each of the 230 images in each database, we col-
lected the set of discrete values (ratings) assigned by
the observers. In this section we analyze the normal-
ized frequencies (with respect to the total number of
observers) of the subjective quality class (from one
to five stars) for each objective quality setting, ob-
tained for MMSP-FaceA and MMSP-FaceB database
respectively. Let us first consider the influence of the
background on the subjective perception of the qual-
ity classes. We compare the experimental results from
MMSP-FaceA and MMSP-FaceB dataset in Figure
3. The normalized frequencies are shown for each
of the quality settings. The objective quality settings
are indicated as follows: 5 (original image), 4 (image
JPEG-compressed with q-factor=30), 3 (q-factor=20),
2 (q-factor=15) and 1 (q-factor=10). We observe that
the most distorted images are labeled with one star
more frequently when no background is present (75%
versus 70%). A similar behavior of the data can be
observed for quality settings 2, 3 and 4. In fact, even
if the differences decrease, for these quality settings
, the corresponding images are more frequently rated
as two, three and four stars in absence of background:
53% vs 46%, 38% vs 35% and 36% vs 32% respec-
tively. However, with respect to the original pristine
images, these were more frequently labeled with five
stars when the background was present (63% versus
56%). We now investigate how do the two emotion
categories (happy-sad) influence the quality assess-
ment at different levels of JPEG compression. In
Figure 4 we plot again the normalized frequency as
a function of the objective quality settings but now
considering separately the subset of happy and sad
images respectively. In the presence of background
(first column of Figure 4), the most distorted images
from the sad subset show a higher probability to be
labeled as one star (72%) with respect to the happy
images (67%). This difference becomes smaller when
the background is not present (second column of the
Figure), where the corresponding probabilities are 76
% (sad) and 74 % (happy) respectively. On the other
side, we observe that the probability of pristine im-
ages to be classified with five stars is greater in the
case of happy images compared to sad ones, in both
databases. In the case of MMSP-FaceA data (first col-
umn of Figure 4) we have: 66 % versus 59%, while
for the MMSP-FaceB data (second column of the fig-
ure), we have: 60% against 51%. For quality settings
2 and 4, the probability of happy images to be labeled
as two and four stars respectively are slightly higher
than the corresponding sad ones, for both databases.
In what follows, the final subjective quality class as-
signed to a given image is the class with highest fre-
quency among all the participants. Using the statis-
tical mode we discard the influence of outliers. Be-
sides aggregating the subjective data using the statis-
tical mode, we also evaluate the Mean Opinion scores
(MOS) as traditionally done in the field of IQA. In
Figure 5 we compare the MOS obtained using the
two different databases. A high correlation is found
between the subjective quality perception in both ex-
periments; the linear Pearson correlation coefficient is
equal to 0.97.
4 OBJECTIVE FEATURES
One of our goal is to predict the ve quality classes
(from one to five stars) using features computed from
the images themselves (i.e. objective features). In this
section we list and briefly describe the set of consid-
ered features. These features will be used in the next
section to train and test a quality classifier. We have
considered two groups of features: features obtained
from No Reference (NR) quality metrics (subsection
4.1), and visual features (subsection 4.2).
4.1 No Reference Quality Metrics
We consider fourteen NR qualtiy metrics frequently
used in the literature: seven are specific to measure
JPEG distortions (metrics M1 to M7) and the rest are
general purpose ones (metrics M8 to M14). A brief
description of them follows:
M1 (Wu and Yuen, 1997): It is the most well
known metric in the spatial domain. It measures the
SIGMAP 2018 - International Conference on Signal Processing and Multimedia Applications
388
Figure 3: Normalized frequencies (with respect to the total number of observers) are plot for each of the objective quality
settings on MMSP-FaceA and MMSP-FaceB dataset respectively. The objective quality settings are: 5 (original image), 4
(image JPEG-compressed with q-factor=30), 3 (q-factor=20), 2 (q-factor=15) and 1 (q-factor=10).
Figure 4: Normalized frequencies (with respect to the total number of observers) for each of the objective quality settings in
the subset of happy and sad images for MMSP-FaceA and MMSP-FaceB dataset respectively. The objective quality settings
are: 5 (original image), 4 (image JPEG-compressed with q-factor=30), 3 (q-factor=20), 2 (q-factor=15) and 1 (q-factor=10).
Figure 5: Correlation between the MOS obtained for
MMSP-FaceA and MMSP-faceB database.
blockiness separately in horizontal and vertical direc-
tion, after which the two directions are combined into
a single quality value.
M2 (Babu et al., 2004): it integrates edge am-
plitude, edge length, background activity and back-
ground luminance to evaluate block edge impairment.
M3 (Wang et al., 2000): It is formulated in the
frequency domain. It models the blocky image as a
non-blocky image interfered with a pure blocky sig-
nal. It detects and evaluates the peaks in the power
spectrum of the blocky signal.
M4 (Wang et al., 2002): The method works in the
frequency domain and is based on gradient features.
It considers blurring and blocking as the most signifi-
Quality Assessment of JPEG-distorted Face Images: Influence of Affective Content
389
cant artifacts generated during the JPEG compression
process.
M5 (Pan et al., 2004): It is based on gradient fea-
tures and it examines the blocks individually, measur-
ing the severity of blocking artifacts locally. The local
metric is averaged over all possible blocks to yield a
unique score.
M6 (Chen and Bloom, 2010): The absolute dif-
ference between horizontally adjacent pixels is com-
puted, normalized, and averaged along each col-
umn. A one-dimensional discrete Fourier transform
is thereafter employed and vertical and horizontal
blockiness measures are derived.
M7 (Muijs and Kirenko, 2005): The key algo-
rithm is based on the principle that block discontinu-
ities can be characterized as edges that stand out from
the spatial activity in their vicinity. The visibility of a
block edge is determined by the contrast between the
local gradient and the average gradient of the adjacent
pixels.
M8 (Moorthy and Bovik, 2010): the Blind Im-
age Quality Index is a general purpose metric based
on natural scene statistics that does not require any
knowledge of the distorting process. BIQI has been
evaluated on the LIVE database and in particular on
its subset of JPEG-distorted images.
M9 (Mittal et al., 2012): the Blind/Referenceless
Image Spatial QUality Evaluator is a general purpose
metric based on natural scene statistic. It uses scene
statistics of locally normalized luminance coefficients
to quantify possible losses of naturalness in the image
due to the presence of distortions.
M10 (Saad et al., 2010): the BLind Image In-
tegrity Notator using DCT-Statistics (BLIINDS) is a
general-purpose metric that uses natural scene statis-
tics models of discrete cosine transform coefficients
to perform distortion-agnostic NR IQA.
M11 (Mittal et al., 2013): the Natural Image Qual-
ity Evaluator is based on the construction of a qual-
ity aware collection of statistical features based on
a space domain natural scene statistic model without
training on human-rated distorted images.
M12 (Ghadiyaram and Bovik, 2015): it is a deep
belief network that takes model-based statistical im-
age features derived from a very large database of au-
thentically distorted images.
M13 (Liu et al., 2016): Blind IQA by relative gra-
dient statistics and adaboosting neural network.
M14 (Gu et al., 2015): it uses the free energy prin-
ciple for blind IQA together with classical human vi-
sual system-inspired features such as structural infor-
mation and gradient magnitude.
4.2 Visual Features
A total of 21 visual features are considered. We eval-
uate the following group of features usually adopted
for texture analysis and image classification: Coarse-
ness, Contrast, Directionality, Linelikeness, Rough-
ness, (Tamura et al., 1978), Edge density (Mack
and Oliva, 2004), Local Binary Pattern (LBP) (Ojala
et al., 1996), and Histogram of Oriented Gradients
(HOG), developed by Ludwig et al. (Junior et al.,
2009). All these features are 1-Dimensional (1-D),
except LBP (2891-D) and HOG (1296-D). We also
consider a group of features related to simple chro-
matic properties: Chroma Variance (1-D) (Ciocca
et al., 2016), Number of Regions (1-D) (Comani-
ciu and Meer, 2002), Colorfullness (1-D) (Hasler and
Suesstrunk, 2003), Color Histogram in the HSV color
space (32-D), color simple statistics (mean and stan-
dard deviation) in the RGB color space, (6-D), Auto-
correlogram obtained quantizing the RGB color space
in 64 colors, (64-D). Finally, we take into account
features related to photographic properties, and visual
perception: Feature Congestion and Subband Entropy
(Rosenholtz et al., 2007), image complexity (Corchs
et al., 2016), entropy, edge contrast and measure of
enhancement (Schettini et al., 2010), a measure of the
degree of focus (Minhas et al., 2009), all of them 1-
D and a 113-D aesthetic feature vector (Bhattacharya
et al., 2013).
5 CLASSIFICATION RESULTS
As pointed out in the introduction, we here propose to
classify images within ve quality classes instead of
using regression models as traditionally done in IQA
literature. A similar framework was previously pro-
posed in (Corchs et al., 2014). The features within
each group, either quality metrics or visual, are con-
catenated together forming a single feature vector of
size 14 and 4412 respectively (many of the visual
features are multidimensional as indicated in section
4.2). The ground truth labels are the subjective quality
classes (from one to five stars) obtained from the ex-
perimental session conducted on MMSP-FaceA and
MMSP-FaceB database. We recall that for each of
the 230 images a subjective quality class was as-
signed using the mode statistic. We report in Ta-
ble 1 the classification results obtained using Support
Vector Machine (SVM) classifiers for MMSP-FaceA
database. The best performance (in terms of accu-
racy) was achieved using a linear kernel in case of
visual features and a quadratic kernel in case of qual-
ity metrics. A five-cross validation scheme was ap-
SIGMAP 2018 - International Conference on Signal Processing and Multimedia Applications
390
plied. We observe that using the quality NR metrics
as feature space of the SVM classifier outperforms
the corresponding classification using visual features.
The confusion matrices are shown in Tables 2 and
3, where the number of images of each quality class
are reported. Analyzing in detail the confusion ma-
trix from Table 2 we can note that quality class five
is the best predicted (only 3 images were misclassi-
fied). Quality class four is not predicted at all, and
we observe that is the class less populated (only 19
images were labeled with 4 stars). Images of qual-
ity class three are mainly misclassified in quality two.
Also for quality two, high rates of misclassifications
are observed. With respect to quality class one, 70%
of correct predictions are found. Similar results are
observed from the confusion matrix in Table 3 but the
number of misclassifications is decreased in particu-
lar for qualities two and three. We repeat the classi-
fication task but considering separately the subset of
happy and sad images. The corresponding accuracies
are shown in Table 1 . As in the case of the whole
database, the same conclusion is achieved: the per-
formace of classification is higher when considering
the quality metrics’ space. For each feature space we
have that: for visual features, both subsets show a de-
crease of classification performance with respect to
the whole database; this decrease is more evident for
the sad subset of images. With respect to the qual-
ity metrics’ space, the classification performance is
slightly increased for both happy and sad subsets with
respect to the whole dataset.
Table 1: Performance in terms of accuracy (%) of SVM
classifiers considering MMSP-FaceA.
Features MMSP-FaceA HAPPY SAD
Visual 59 57 47
Quality 72 73 73
Table 2: Confusion matrix obtained using SVM classifier
with visual features on MMSP-FaceA.
Class Predicted
Real 1? 2? 3? 4? 5?
1? 42 16 1 0 0
2? 14 36 12 0 0
3? 3 26 21 0 1
4? 0 3 9 0 7
5? 0 0 1 2 36
We performed the same experiments on the
MMSP-FaceB database. Classification accuracies are
shown in Table 4 and the corresponding confusion
matrices for the whole dataset are reported in Tables
5 and 6. Even if in general similar classification per-
formances are obtained, the distribution of true posi-
Table 3: Confusion matrix obtained using SVM classifier
with quality metrics features on MMSP-FaceA.
Class Predicted
Real 1? 2? 3? 4? 5?
1? 46 13 0 0 0
2? 3 51 8 0 0
3? 0 19 31 0 1
4? 0 1 11 1 6
5? 0 0 1 0 38
tives and misclassifications has changed. In particu-
lar, from Table 5, comparing the present results with
those of MMSP-FaceA data (Table 2), class 2 seems
now to be better predicted while class 5 confirms to
be the one best predicted. Neither in this case class
4 is predicted. From the comparison of Tables 3 and
6, the distribution of true and false positivies does not
show important differences. Finally, with respect to
the comparison of the whole dataset MMSP-FaceB
and the happy and sad subsets (Table 4 ) we note that,
considering the quality metrics’ feature space, the sad
(happy) images present a higher (lower) classification
performance with respect to the whole dataset.
Table 4: Performance in terms of accuracy (%) of SVM
classifiers considering MMSP-FaceB.
Features MMSP-FaceB HAPPY SAD
Visual 57 54 50
Quality 72 69 75
Table 5: Confusion matrix obtained using SVM classifier
with visual features on MMSP-FaceB.
Class Predicted
Real 1? 2? 3? 4? 5?
1? 29 15 2 0 0
2? 7 50 13 0 0
3? 1 20 20 5 0
4? 0 7 16 1 8
5? 0 0 2 3 31
Table 6: Confusion matrix obtained using SVM classifier
with quality metrics features on MMSP-FaceB.
Class Predicted
Real 1? 2? 3? 4? 5?
1? 39 7 0 0 0
2? 3 56 11 0 0
3? 0 10 32 3 1
4? 0 2 17 3 10
5? 0 0 1 0 35
As a final experiment, let us apply our quality
classification proposal on the database from (Van
Der Linde and Doe, 2012), composed of JPEG-
compressed images that also include different affec-
tive contents. The authors considered 100 of the 730
Quality Assessment of JPEG-distorted Face Images: Influence of Affective Content
391
Table 7: Performance in terms of accuracy (%) of SVM
classifiers, considering the dataset of Van Der Linde.
Features (Van Der Linde and Doe, 2012)
Visual 33
Quality metrics 35
affective color images provided in the Geneva Af-
fective PicturE Database (GAPED) (Dan-Glauser and
Scherer, 2011). Images ”negative” (spiders, snakes,
human concerns), ”positive” and ”neutral” were cho-
sen. For each of the original images, four JPEG com-
pressed versions were generated (q-factors 10, 15, 20
and 30). The final database is composed of 500 im-
ages. Each of the 500 stimulus images was rated by 5
participants. Since the authors set up the experiment
using a 10 points discrete scale, we had to collapse
their 10 points scale to our ve points one. There-
fore, we have reordered their results as follows: (1-2)
was assigned to label 1, (3-4) to label 2, (5-6) to la-
bel 3, (7-8) to label 4 and (9-10) to label 5. The final
label of each image is obtained applying the statisti-
cal mode. In Table 7 we show the classification re-
sults. The low accuracies observed in this case can
be due to several factors. While our database con-
tains only images belonging to a unique semantic con-
tent (face), the database by (Van Der Linde and Doe,
2012) contains different semantic contents (animals,
humans, etc.). Another factor that could explain the
differences is that our database contains images rep-
resenting only happy and sad emotions, while the im-
ages used by (Van Der Linde and Doe, 2012) span
many different values in the valence-arousal space of
emotions. Finally, with respect to the authors’ main
conclusion, i.e. that the pleasantness of an image (its
valence) is a significant factor that influences subjec-
tive rating, we can conlcude from our experimental
data that the main influences (differences) are found
when assessing the quality of original pristine images,
while these differences decrease as the level of distor-
tion increases.
6 CONCLUSIONS
From our experimental results we can conclude that
the influence of affective content on the subjective
perception of image quality is mainly observed when
images are of very high or very low quality. In par-
ticular, happy images in their pristine version were
more frequently rated as best quality compared to sad
pristine images. On the contrary, sad images in their
most distorted version were more frequently rated as
worst quality compared to the most distorted version
of the happy ones. The quality perception was also
influenced by the background of the images. When
such background was eliminated and only the faces
were used as stimuli, the frequency of ratings of pris-
tine images as best quality was lower than in the ex-
periment where background was present, while im-
ages most distorted without the background were as-
signed to the worst quality more frequently compared
to the stimuli with background. The classification ap-
proach using SVM and a feature space composed of
NR quality metrics was able to predict the five qual-
ity classes with an accuracy of 72%. Different issues
have to be considered in the near future. Increasing
the cardinality of the database is important both from
the subjective perception side and also to better apply
machine learning classification strategies. Moreover
other quality metrics for JPEG distortions proposed in
the last years can be considered and a proper feature
selection strategy will be also considered to reduce
redundancies in the chosen metrics. Deep learning
based classifiers will also be taken into account in the
next future.
ACKNOWLEDGEMENTS
We gratefully acknowledge the support of NVIDIA
Corporation with the donation of the Tesla K40 GPU
used for this research. We acknowledge Giorgio Pi-
lotti for his assistance during the experimental ses-
sions.
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