SUGISAKAMASANORI

Journal of Advances in Artificial Life Robotics
Vol. 4(1); June (2023), pp. 55–58
ON LINE ISSN 2435-8061; ISSN-L 2435-8061
https://alife-robotics.org/jallr.html

Research Article

Haze Predication Based on Image Quality Score
Heshalini Rajagopal1, Sayanth Sudheer2, Norrima Mokhtar3

Institute of Computer Science and Digital Innovation, UCSI University, 56000 Kuala Lumpur, Malaysia
Department of Electrical and Electronic Engineering, Manipal International University, Malaysia
Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Malaysia

123ARTICLE INFO
Article History
Received 06 August 2022
Accepted 08 September 2023
Keywords
Haze
Image quality assessment (IQA)
Image quality score
Haze prediction

ABSTRACT

Haze is a prevalent term within the field of image processing, encompassing both naturally
occurring phenomena and aerosols generated by human activities. It gives rise to light scatteringand absorption, leading to reduced image visibility. This diminished clarity poses challenges forvarious photographic and computer vision applications, including object recognition andlocalization. Consequently, there is a growing need for a method to estimate haze densityaccurately. In this research paper, we introduce a novel model called the "haziness degreeevaluator." This model enables the prediction of haze density from a single image, eliminating thenecessity for a reference haze-free image. The proposed model quantifies haze density throughthe optimization of an objective function that encompasses haze-related features derived fromcorrelation and computational analysis.

© 2022 The Author. Published by Sugisaka Masanori at ALife Robotics Corporation Ltd.
This is an open access article distributed under the CC BY-NC 4.0 license(http://creativecommons.org/licenses/by-nc/4.0/).

density [2]. Outdoor photographs are frequently
marred by haze, an atmospheric phenomenon
generated by minute airborne particles that bothabsorb and scatter light in various directions. Haze
has a detrimental impact on image visibility,
particularly evident in the loss of contrast for distantobjects within the image [3]. In the realm of computer
vision applications, dehazing techniques areemployed to enhance the visibility of outdoor imagesby mitigating the undesirable effects resulting fromlight scattering and absorption due to atmosphericparticles. Dehazing is indispensable for an array ofhuman activities and numerous algorithms, including
object recognition, object tracking, remote sensing,and sometimes computational photography. In
scenarios characterized by poor visibility, suchapplications necessitate dehazed images to achieve
optimal performance. Consequently, researchendeavors have focused on haze removal to enhance
the quality of degraded images [3]. Furthermore, theassessment of image quality serves as a pivotal gauge
for determining the extent of degradation and

1. Introduction
Haze is a well-known atmospheric occurrence
characterized by the presence of dust, smoke, andvarious dry particulates that obscure the clarity of thesky. Haze particles, in certain cases, can have adverse
effects on the cardiovascular and respiratory systems,
particularly in individuals already dealing withchronic heart or lung conditions such as asthma,
Chronic Obstructive Pulmonary Disease (COPD), orheart failure. Health effects or symptoms following
exposure to haze may manifest with a delay of one tothree days [1]. The density of haze, often referred toas "see-through quality," is conventionally assessedusing a narrow-angle scattering test. In this test, lightis diffused within a limited angle range with highconcentration to gauge the degree of clarity throughthe subject under examination. This method, widelyadopted across industries, employs specialized
apparatus like ASTM E430, ASTM D4039, and ISO
13803, resembling microscopes, to quantify haze

Corresponding author’s E-mail: heshalini@ucsiuniversity.edu.my, norrimamokhtar@um.edu.my URL: www.ucsiuniversity.edu.my55

improvement. Image Quality Assessment (IQA) is awell-established domain within the field of image
processing, categorized into two primary methods:
subjective and objective. The subjective methodseeks to capture human perception of image quality,
simulating the perceptual processes of the humanvisual system to estimate it. The outcomes of thismethod are translated into Mean Opinion Scores(MOS), which are subsequently employed toformulate the objective method, namely, the proposed
IQA metric [4], [5].

𝛽𝛽 = 𝜎𝜎�

12𝛽𝛽Γ� �𝛼𝛼|𝑥𝑥| 𝛼𝛼exp �− � � �𝛽𝛽Where 𝑥𝑥 represents the MSCN and∞(3)The AGGD namely 𝑣𝑣, which represents the shape of thedistribution, 𝜎𝜎𝑙𝑙2 and 𝜎𝜎𝑟𝑟2 which represent the left andright-scale parameters, respectively, and η which
represents the mean of the distribution. using the Eqs.
(4), (5), (6), (7):
𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴(𝑥𝑥; 𝑣𝑣, 𝜎𝜎𝑙𝑙2 , 𝜎𝜎𝑟𝑟2 , 𝜂𝜂) =This paper introduces a model known as the "haziness
degree evaluator," designed to predict haze density froma single image without the need for a corresponding hazefree reference image. The proposed model quantifies
haze density through the optimization of an objectivefunction that incorporates relevant haze-related featuresderived from correlation and computational analysis. Tovalidate the model's accuracy, the Mean Opinion Score(MOS) was initially obtained from twenty humansubjects for hazy images [4]. These MOS values were
subsequently compared to various Image Quality
Assessment(IQA)
metrics,
includingBlind/Referenceless Image Spatial Quality Evaluator(BRISQUE) [7], Naturalness Image Quality Evaluator(NIQE) [8], Structural Similarity Index (SSIM) [9],Feature Similarity (FSIM) [10], and Gradient Magnitude
Similarity Deviation (GMSD) [11]. Next, Gaussianfeatures were extracted from the hazy images, and inconjunction with the validated MOS values, they wereinput into a Support Vector Machine (SVM) Regression(SVR) model. The purpose was to train the machine toestablish a mapping between these features and the MOSvalues, ultimately yielding an optimized model. This
optimized model was then employed to predict thequality scores for test hazy images, which, in turn, wereused to predict haze density within the images.
The computation process began with the calculation of
the Mean Subtracted Contrast Normalized (MSCN) forthe hazy images [6]. Two distinct types of Gaussian
distribution functions, the Generalized GaussianDistribution (GGD) and Asymmetric Generalized
Gaussian Distribution (AGGD), were incorporated into
this study to account for the varied characteristics of theMSCN coefficients [5]. The GGD, characterized byparameters α (representing the distribution's shape) andσ2 (representing the variance), along with AGGD
parameters, were calculated Eqs. (1), (2), (3):𝛼𝛼(2)3𝛼𝛼Γ� �Γ(𝑎𝑎) = ∫0 𝑡𝑡 𝑎𝑎−1 𝑒𝑒 −𝑡𝑡 𝑑𝑑𝑑𝑑, 𝑎𝑎 > 02. Methodology

𝐺𝐺𝐺𝐺𝐺𝐺(𝑥𝑥; 𝛼𝛼, 𝜎𝜎 2 ) =1Γ�𝛼𝛼�𝑣𝑣−𝑥𝑥 𝑣𝑣⎧ (𝛽𝛽 +𝛽𝛽 )Γ�1� exp �− � 𝛽𝛽𝑙𝑙 � �𝑙𝑙𝑟𝑟𝑣𝑣

𝑣𝑣

𝑣𝑣
𝑥𝑥
⎨1 exp �− � � �𝛽𝛽𝑟𝑟⎩(𝛽𝛽𝑙𝑙 +𝛽𝛽𝑟𝑟 )Γ�𝑣𝑣�

𝑥𝑥 < 0𝑥𝑥 ≥ 0(4)Where 𝑥𝑥 is the MSCN calculate at four neighborhood
pixels and𝛽𝛽𝑙𝑙 = 𝜎𝜎𝑙𝑙 �1(5)

1(6)

Γ�𝑣𝑣�
3𝑣𝑣

Γ� �𝛽𝛽𝑟𝑟 = 𝜎𝜎𝑟𝑟 �Γ�𝑣𝑣�
3𝑣𝑣

Γ� �𝜂𝜂 = (𝛽𝛽𝑟𝑟 − 𝛽𝛽𝑙𝑙 )(1)

562

Γ�𝑣𝑣�
1𝑣𝑣

Γ� �(7)

The flowchart of the proposed haze prediction system isshown in Fig.1

Table 1. PLCC between MOS and NIQE, BRISQUE,SSIM, FSIM and GMSD
FRIQAsNIQE

BRISQUE

SSIM

FSIM

GMSD

PLCC

0.819

0.895

0.832

0.788

0.900

In line with Taylor's findings (Taylor, 1990), a highcorrelation between two datasets is typically indicatedwhen the correlation coefficient values fall within therange of 0.68 to 1.0 [5]. Given that all the calculated
PLCC values surpass the threshold of 0.68, it
demonstrates the validity of the MOS values obtained
from the subjective evaluation. Consequently, these
MOS values can be considered reliable and suitable fortraining the Support Vector Regression (SVR) model.
3.2. Performance of the Proposed System
The effectiveness of the proposed system was assessedby evaluating the Pearson's Linear Correlation
Coefficient (PLCC) between the MOS and various IQA
metrics, including NIQE, BRISQUE, SSIM, FSIM,
GMSD, and the newly proposed IQA metrics, as outlinedin Table 2. According to the data presented in Table 2,
the newly proposed IQA metric outperformed the other
IQA metrics, namely BRISQUE, NIQE, SSIM, GMSD,and FSIM, by achieving the highest PLCC value. Thisoutcome underscores the superiority of the proposed IQA
metric in evaluating hazy images, as it closely aligns withthe MOS values.
Table 2. PLCC between MOS and NIQE, BRISQUE,SSIM, FSIM, GMSD and proposed IQA.

Fig.1.Flowchart of the system

IQA

NIQE

BRISQUE

SSIM

FSIM

GMSD

PLCC

0.819

0.895

0.832

0.788

0.900

4.

3. Findings

ProposedIQA0.970

Conclusion

The primary focus of this paper is the development of an
IQA system tailored for the automated assessment ofhaze density within images. This system represents anefficient and economical solution compared to traditional
methods that involve the use of specialized sensors forhaze detection. By sidestepping the requirement for suchsensors, this approach not only reduces costs but also
simplifies the deployment of the system, making it
accessible in various practical scenarios.
Moreover, the distinguishing feature of this IQA systemlies in its ability to determine haze density autonomously,

3.1. Validation of MOS
The Mean Opinion Score (MOS) values gathered fromthe assessment of twenty human subjects were subjected
to validation using established Image QualityAssessment (IQA) metrics, specifically NIQE,
BRISQUE, SSIM, FSIM, and GMSD. The degree ofcorrelation between the MOS and these IQAs wasdetermined through the calculation of Pearson's Linear
Correlation Coefficient (PLCC). The resulting PLCC
values for the correlation between MOS and IQAs arepresented in Table 1.
57

without the necessity of having reference images forcomparison. Many existing methods rely on comparing
hazy images with their haze-free counterparts to estimate
haze density. However, this newly proposed system
breaks free from this dependency, making it versatile andsuitable for situations where reference images may not be
available or practical to use. This attribute enhances thesystem's applicability and robustness in real-world
applications where accurate haze density assessment iscrucial.

Authors Introduction
Dr. Heshalini Rajagopal
She received her PhD and Master's
degree from the Department ofElectrical Engineering, University ofMalaya, Malaysia in 2021 and 2016,respectively. She received the B.E(Electrical) in 2013 Currently, she isan Assistant Professor in UCSI
University, Kuala Lumpur, Malaysia.
Her research interest includes image
processing, artificial intelligence and machine learning.

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Mr. Sayanth SudheerHe received his Diploma from theDepartment of Electrical and ElectronicEngineering, Manipal International
University,Malaysiain2022.
Currently, he is an Intern in Splatz Zone
Sdn Bhd, Malaysia.

Dr. Norrima Mokhtar
She received the B.Eng. degree fromUniversity of Malaya, the M.Eng. andthe Ph.D. degree from Oita Univerity,
Japan. She is currently a SeniorLecturer in the Department ofElectrical Engineering, University ofMalaya. Her research interests aresignal processing and human machineinterface.

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