High Dynamic Range (HDR) Image Quality Assessment: A Survey

Ocarina Cloramidina, Salmaa Badriatu Syafa’ah, Irwan Prasetya Gunawan, Guson Prasamuarso

Kuntarto, and Berkah Iman Santoso

Informatics Engineering, Faculty of Engineering and Computer Science, Universitas Bakrie, Jakarta 12920, Indonesia

Keywords:

Image Quality Assessment (IQA), High Dynamic Range (HDR), Reduced-Reference (RR), Multi Exposure

Fusion (MEF), Inverse Tone Mapping Operator (ITMO)

Abstract:

This paper presents a survey on objective image quality measurement method for High Dynamic Range (HDR)

images. The emergence of HDR technology requires HDR image quality evaluation techniques to help max-

imize user satisfaction. In spite of its progress, HDR images have put more difﬁcult challenges in quality

evaluation due to high sensitivity of Human Visual System (HVS) to the appearance of distortions in HDR

images. Several image quality assessment methods for HDR images will be discussed. It was found that for

HDR IQA, previous works in the literature are still focused on the full reference and no reference methods.

Therefore, there are some possibilities to develop reduced reference method for HDR IQA.

1 INTRODUCTION

High Dynamic Range (HDR) imaging is an advanced

visual-based technology capable of providing better

visual information representation for human view-

ers.Currently, there are various methods to form HDR

images from Low Dynamic Range (LDR) or Stan-

dard Dynamic Range (SDR) images; in general, they

can be categorized into Multi Exposure Fusion (MEF)

and Inverse Tone Mapping Operator (ITMO) algo-

rithms. MEF captures a series of images with dif-

ferent levels of exposure as input and combines them

into an output image with more information to show

and more attractive than any of the input images. On

the other hand, ITMO restores HDR information from

LDR/SDR image. These methods, however, may in-

troduce artifacts that can degrade the resulting visual

quality of the image. The emergence of HDR tech-

nology requires HDR image quality evaluation tech-

niques to help maximize user satisfaction. In spite

of its progress, HDR images have put more difﬁcult

challenges in quality evaluation due to high sensitiv-

ity of human eye to the appearance of distortions in

HDR images.

In general, image quality assessment (IQA) can

generally be divided into two methods: subjective and

objective. Subjective methods rely on human sub-

ject and hence are considered reliable; however, they

could become very expensive and time consuming.

Objective quality method predicts image quality au-

tomatically without human intervention. Depending

on the availability as well as the accessibility of the

original images, this method can be categorized into

full reference (FR), reduced reference (RR), and no

reference (NR) methods (VQEG, 2000; VQEG, 2002;

VQEG, 2004). The objective model can be differen-

tiated based on the method to ‘quantify’ the quality.

There are methods based on error differences (Nar-

waria et al., 2015), structural information (Yeganeh

and Wang, 2013); (Aydin and Seidel, 2008); (Ma

and Wang, 2015), and even machine learning (Jia and

Bull, 2017).

For LDR/SDR images, various methods in FR,

NR, and RR have been around for quite some time.

However, for HDR imaging, there are plenty of

FR/NR methods but not many on RR.

Based on the explanation above, the present study

will survey the objective quality evaluation for HDR

images.

The rest of the paper is organized as follows. In

the next section, we will brieﬂy describe typical HDR

imaging pipeline. Subsequently, in Section 3 we will

discuss image quality assessment in general, followed

by Section 4 that will outline some of the existing

HDR IQA models in the literature. This will then be

followed by conclusion in Section 5.

Gunawan, I., Cloramidina, O., Syafa’ah, S., Kuntarto, G. and Santoso, B.

High Dynamic Range (HDR) Image Quality Assessment: A Survey.

DOI: 10.5220/0009354900330040

In Proceedings of the International Conferences on Information System and Technology (CONRIST 2019), pages 33-40

ISBN: 978-989-758-453-4

Figure 1: HDR imaging pipeline((Artusi and Mantiuk, 2017); (Mantiuk and Seidel, 2016)

2 HDR IMAGING PIPELINE

An illustration into HDR image and video processing

is given in Figure 1. It depicts an image pipeline from

acquisition through compression and quality evalua-

tion (Artusi et al., 2017; Mantiuk et al., 2016) in a

real world scene or in a rendered computer model.

Firstly, the HDR images are generated either by com-

puter graphic which makes a scene or captured form

a real world scene by a camera. Afterward, the im-

ages are compressed and encoded for storage or trans-

mission purposes. The encoding-decoding processes

are performed to convert the image into a more efﬁ-

cient data format which requires less storage capacity.

Then, the images are visualized in a display device.

HDR content visualization is still limited by the

ability of the devices. To capture the dynamic range

of HDR by the lower speciﬁcation device, tone map-

ping is employed. Other techniques such as color cor-

rection may also be used to handle the mismatches

between the HDR content and the display devices.

Conversely, there is also an Inverse Tone Mapping al-

gorithm with which an HDR content is reconstructed

from a single SDR content, and Multi-Exposure Im-

age Fusion (MEF) method that capable of generating

HDR images from SDR images.

Last but not least, the quality assessment of an im-

age or video is performed. The main goal of the qual-

ity assessment is to verify the algorithms of the stages

in the pipeline.

3 IMAGE QUALITY

ASSESSMENT

The demand for image-based applications is increas-

ing and causing the growing importance of the efﬁ-

cient and reliable image quality evaluation (Moham-

madi and Shirani, 2014). There has been a rising

number of techniques and algorithms to perform im-

age quality assessment (IQA). IQA has found its us-

age in various applications; for example, image and

video coding, digital watermarking, denoising, image

synthesis, and many other areas. IQA can be used

for various purposes: quality monitoring, benchmark-

ing, or optimization in multimedia processing sys-

tems (Thung and Raveendran, 2009).

Imaging system can introduce a number of dis-

tortions or artifacts to the signal; this is an impor-

tant problem in the aspect of IQA (Patil and Sheel-

vant, 2015). The evaluation of human perception

comfort, namely Quality of Experience (QoE), is the

main objective of quality measurement of images

CONRIST 2019 - International Conferences on Information System and Technology

and videos (Opozda and Sochan, 2014). It was also

pointed out that image quality is affected by both ob-

server’s attributes and technical properties of presen-

tation (Opozda and Sochan, 2014).

Depending on how the evaluation is performed,

IQA can be achieved in one of two ways: subjective

and objective quality methods. Each of these methods

has its own unique way of evaluation. The following

sub-sections will describe a little bit more about these

evaluation methods.

3.1 Subjective Methods

Subjective quality assessment is a controlled exper-

iment with human participants used to measure per-

ceptual quality. In subjective assessment, human

judges are the golden standard without the advice of

others (Patil and Patil, 2017). Not only that, subjec-

tive methods can also (Ma et al., 2015):

• Provide data which is useful in the study of hu-

man behavior in the evaluation of image quality

perceptions

• Provide a set of tests to assess the relative per-

formance of various image processing algorithms

and methods and compare them

• Be used to assess the relative performance of ob-

jective Image Quality Assessment (IQA) models

that exist today in the prediction of integrated im-

age perceptual quality. This will also provide in-

sight into possible ways of improving it.

Based on the use of stimuli, subjective methods

are classiﬁed into single stimulus and double stimu-

lus (Patil and Patil, 2017). There are many different

methodologies and rules for the design of subjective

quality evaluation tests. Usually, in this type of as-

sessment, a number of observer are subjected to im-

ages with various degree of different quality, and are

asked to provide their quality evaluation of these im-

ages. Scores that are assessed by numerous subjects

arrive at the midpoint for each image to get an average

guess score.

Subjective method possess several drawbacks

(Hands, 1998):

• It takes time and money for the test;

• Recruit and pay for subjects;

• The equipment used must be tested and regulated;

• Laboratory;

• Experiments must run tests.

Subjective tests are usually complicated, imprac-

tical, and not feasible for certain applications (Win-

kler, 2005). People turn to objective tests for faster

and more practical results. Subsequently, objective

assessments are tested and veriﬁed based on selected

subjective judgments as the ground truth data.

3.2 Objective Methods

In numerous audio-visual services, objective mea-

surements are used to assess the inﬂuence of the cod-

ing system and transmission path on the quality of

multimedia data presentations. The goal is to for-

mulate mathematical models that automatically and

accurately predict the quality of the image. Some-

times the use of subjective judgments is necessary for

objective measurement that is appropriate as a refer-

ence evaluation. This allows precise measurements

for certain types of distortion: blur image, blurred

motion, edge, false contouring, granular noise, jerk-

iness, blockage, dirty window. Objective IQA is ap-

plied in various applications such as (Mohammadi

and Shirani, 2014):

• image quality monitoring in quality control sys-

tem

• image processing algorithms and systems com-

parison

• image processing and transmission systems opti-

mization

The Video Quality Expert Group (VQEG) has de-

ﬁned three distinct methods for image/video quality

assessment based on the availability of reference im-

age: full reference (FR), reduced reference (RR), and

no reference (NR) methods (VQEG, 2000), (VQEG,

2002), (VQEG, 2004). These methods will be ex-

plained in the following Secio,

3.2.1 Full-Reference (FR)

Full-Reference (FR) method evaluates the perfor-

mance of the systems by comparing the undistorted

signal at the system input with the degraded signal at

the system output (Opozda and Sochan, 2014). In this

situation, the human visual system requires an allu-

sion sample to deﬁne an image’s excellent level (Patil

and Patil, 2017).

3.2.2 No-Reference (NR)

No-Reference (NR) image quality model makes use

of characteristics of HVS. Human eye does not need

a source test or is based only on the processed image

where the reference image is not available to deter-

mine the level of excellence of the image (Opozda and

Sochan, 2014); (Patil and Patil, 2017). The animation

quality rating scheme has no access to reference im-

ages in many tactical apps. It is therefore anticipated

High Dynamic Range (HDR) Image Quality Assessment: A Survey

that a measurement method will be developed that can

blindly assess image quality. NR method is also re-

ferred to as “blind models” (Patil and Patil, 2017).

The blind image quality size is difﬁcult to design, but

it is more useful than a reference image.

3.2.3 Reduced-Reference (RR)

RR image quality assessment provides a useful solu-

tion method between the FR and NR quality assess-

ment approaches. They are designed with only partial

data about the reference pictures to predict percep-

tual image quality. Reduced-Reference (RR) method

evaluates system performance by comparing features

extracted from the undistorted signal at system input

with features extracted from the degraded signal at

system output (Gunawan, 2006) (Opozda and Sochan,

2014). The concept of RR quality assessment was

ﬁrst suggested as a means of tracking the degree of

visual quality degradation of video information trans-

ferred through complicated communication networks.

The data rate used to encode side information

is a signiﬁcant parameter in RR quality evaluation

schemes. If a high RR data rate is accessible, then

a big quantity of information about the reference im-

age can be included. If the data rate is big enough to

convey all the reference picture information, the re-

ceiver side can use the FR technique. While the RR

data rate is small, it is possible to send only a small

side information about the reference image. Some de-

sirable properties of RR characteristics are as follows:

• They should provide an efﬁcient overview of the

reference image

• They should be susceptible to various distortions

of image, and

• They should have excellent perceptive signiﬁ-

cance.

4 HDR IMAGE QUALITY

ASSESSMENT

In this section, we will outline some of the previ-

ous HDR image quality assessment models. The re-

view will be limited to cover the essential elements

in a full-reference and no-reference methodologies by

way of some examples.

4.1 HDR-VDP and HDR-VDP-2

HDR Visual Difference Predictor (HDR-VDP) (Man-

tiuk and Seidel, 2005) and its successor, HDR-VDP2

(Mantiuk and Heidrich, 2011) are two examples of

full reference model based on error/difference met-

ric that can predict perceived differences between

two images and, accordingly, the quality. The met-

rics were derived from several visual models capable

of measuring new contrast sensitivity for all lighting

conditions. As such, the models were calibrated and

tested against various contrast discrimination datasets

with arbitrary lighting range; e.g. LIVE database

(, 2006) and TID2008 (Ponomarenko and Battisti,

2009).

The model consists of alternative components that

were tested against a set of psychophysical measure-

ments, and the best components were selected and

adjusted to best ﬁt the data. By doing this way, the

model was able to predict any differences between

two images as if it was observed by human viewer.

The components are:

• Psychophysical model that allows for the creation

of initial vision model;

• Advanced visual models for tone-mapping im-

ages;

• Quality metrics used predict the severity of the

image distortion

• Feature invariant metrics based on structural sim-

ilarity

Some of the components mentioned earlier are im-

portant elements to encourage the creation of all the

expected priorities. The ﬁrst priority in their exper-

iment is accurate matching with experimental data,

whilst the second priority is computational complex-

ity, and then followed by the actual biological mech-

anism for reasonable modeling. The predictor of vi-

sual difference consists of two identical visual mod-

els; each of which is used to process the test and ref-

erence images.

HDR-VDP-2 was shown to have been able to out-

perform its predecessor, and hence it is considered

successful in becoming towards improved visibility

and quality predictors. However, despite its accom-

plishment, there are room for improvement. Model-

ing color vision and temporal processing are the two

main omissions. The temporal domain can be ex-

tended to include spatio-velocity and spatio-temporal

components. Existing achromatic models will beneﬁt

from better spatial integration models, increased sen-

sitivity characteristics for each type of photoreceptor,

and enhanced masking models, calibrated to a wider

set of data. When distortion signals are not exactly

known, metrics can also consider less conservative as-

sumptions.

CONRIST 2019 - International Conferences on Information System and Technology

4.2 TMQI

Tone Mapped Quality Index (TMQI) (Yeganeh and

Wang, 2013) was proposed for objective quality eval-

uation on tone-mapped images. It is yet another full

reference model that combines the multi-scale deriva-

tion of structural similarity approach (SSIM) (Wang

and Bovik, 2003) with a measure of naturalness.

It is very common to visualize HDR images on

a standard screen, resulting in a display of LDR im-

ages, instead. This requires a tone mapping proce-

dure that may cause loss of information due to the

reduced dynamic range. Human viewer who is sub-

jected to the LDR version of the image may not real-

ize this loss, unfortunately. Hence, structural informa-

tion plays a signiﬁcant role for the quality assessment

of tone mapped images. However, structural informa-

tion alone is not enough to provide an overal quality

assessment. In addition to preserving structural de-

tails, statistical properties are also important for get-

ting good quality mapped images.

Therefore, TMQI relies on the structural ﬁdelity

of images as well as statistical naturalness as follows:

• Structural Fidelity, S The SSIM approach is a

practical method used to measure structural weak-

nesses between images. The original SSIM al-

gorithm contains three comparison components,

namely lighting, contrast and also the structure

applied locally.

• Statistical Naturalness, N Naturality is a quantita-

tively difﬁcult to deﬁne subjective quantity. Sta-

tistical models of naturalness are based on statis-

tics at a gray-scale of around 3,000 8bits/pixel

representing different types of natural scenery.

• Quality Assessment Model The structural ﬁdelity

and statistical naturalness described earlier char-

acterizes various aspects of image quality that are

mapped with tones. These two parameters must

be combined in several ways. The TMQI is de-

ﬁned as equation 1.

Q = aS

+ (1 − a)N

(1)

where

– 0 ≤ a ≤ 1 is the relative signiﬁcance of struc-

tural ﬁdelity and statistical naturalness

– α and β determines the sensitivity of each

Since S and N are limited to unity, the overall

quality is also limited in the same way.

4.3 MEF IQA

MEF-IQA (Ma and Wang, 2015) is another full ref-

erence method that is speciﬁcally directed towards

MEFbased images. Similar to TMQI, MEF-IQA is

also based on multiscale structural similarity of MEF

images, but now it is combined with structural consis-

tency. MEF-IQA can also be used to set MEF algo-

rithms parameters.

To evaluate their model, a subjective evaluation

database was created. It consists of 17 sources se-

quences subjected to multiple exposure levels. The

MEF images were created by using eight classic and

sophisticated MEF algorithms.

The output of the quality model and the subjec-

tive data were then compared. This particular IQA

goal has the philosophy of highly adapting HVS to ex-

tract structural information from natural landscapes.

To balance the preservation of detailed scales and the

consistency of coarse luminaries, a multi-scale ap-

proach is used.

In designing and optimizing the new MEF algo-

rithm, a reliable objective model can play a key role.

To demonstrate this potential, the proposed model is

applied to adjust automatic parameters from a sophis-

ticated MEF algorithm. The problem of MEF can be

generally formulated as equation 2

Y (i) =

∑

k=1

(i) (2)

where

• K is the number of images with multiple expo-

sures in the source sequence

• X

(i) luminance value (or transformation domain

coefﬁcient amplitude)

• W

is the i-th pixel weight the k-th exposure image

Eight MEF algorithms from various previous

studies were used in this experiment. These algo-

rithms are chosen based on methodology and behav-

ior that includes various types of MEF methods. MOS

values of 8 MEF algorithms are used for evaluation

and comparison with subjective test performance.

In addition, an examination of how the informa-

tion in the sequence of multi-exposure images is per-

formed in images that are fused at each spatial lo-

cation is based on the general construction of the

SSIM. The SSIM approach results in looking at image

patches from three distinct aspects: lighting, contrast,

and structure.

4.4 DL-NRIQA

This model was proposed by (Jia and Bull, 2017).

They produce No-Reference Image Quality Assess-

ment (NR-IQA) method by combining deep Convo-

lutional Neural Networks (CNNs) with saliency maps

on High Dynamic Range (HDR) images.

High Dynamic Range (HDR) Image Quality Assessment: A Survey

The strength of the CNN architecture is used to

extract quality features that can be used on the SDR

and HDR domains. Input images are broken down

into patches and based on the features presented in

each patch quality patch are carried out on CNN-

based methods. In the proposed method, CNN is only

applied to a subset of patch images that stand out for

evaluation.

For the experiment, they used two different

datasets, SDR and HDR datasets. In SDR datasets,

they use LIVE dataset and CSIQ dataset to learn SDR

quality feature. The HDR dataset is used to train the

proposed method and evaluate its performance.

The steps in their proposed method are as follows:

1. Normalize each image locally using the algorithm

proposed in each dataset

2. Divide each image into a set of small patches of

size 32 x 32 pixels

3. Use salience maps calculated on each image to

set weights for each patch instead of studying net-

work activation weights. Each pixel value of the

salience map is repeated back to the range [0,1].

The addition of pixel values in salient patches

is deﬁned to represent the importance of image

patches.

4. Apply evaluation using the Linear Correlation

Coefﬁcient (LCC) and the Spearman Rank-order

Correlation Coefﬁcient (SRCC).

In the NR-IQA HDR experiment, the CNN-based

method with salient maps provides sophisticated per-

formance, competing with the full IQA reference

method.

4.5 Higrade

One paper discussing other NR models is written by

(Kundu and Evans, 2017). They proposed another

model of NR IQA for HDR images based on band-

pass standard measurements and on differential Nat-

ural Scene Statistics (NSS). The algorithm to be used

is obtained from the HDR Image GRADient Evalua-

tor (HIGRADE). They described the features used in

the model of NR-IQA. These features include estab-

lished descriptors of NSS quality and new features for

processing data in images of the HDR process. Typ-

ically, HDR process artifacts modify the NSS feature

extracted from image gradients. This deviation can be

used to enhance human subjective response predic-

tions. The following are some perceptually relevant

features used in the proposed NR-IQA model:

• Log-Derivatives/Log Gradient feature to predict

the natural image quality that artifacts (non-HDR)

are affected by processing.

• Spatial Domain Scene Statistics that were pro-

cessed with mean subtracted contrast normalized

(MSCN).

• Gradient Domain Scene Statistics for both gra-

dient magnitude and gradient orientation:

1. Gradient Magnitude Features calculated using

a simple Sobel operator to convert the image.

2. Gradient Structure Tensor Features based on

gradient magnitudes

Among the 12 NR-IQA models that were tested,

the proposed HIGRADE algorithms were found

to be the highest performing predictors of human

perceptual judgments of visual HDR artifacts. It

has also been shown that the HIGRADE features

are effective in evaluating the artifacts that arise in

SDR images.

4.6 NR HDR IQA

(Guan and Chen, 2018) proposed a quality rating

method without new references to HDR images. The

tensor space used in their study functions effectively

to deﬁne and extract new HDR image features and

representation space for new HDR features. In ad-

dition, image manifold features also used to evaluate

visual quality can produce results with higher subjec-

tive consistency. From the Tensor Decomposition and

Manifold Learning methods proposed, there are three

main points to become HDR image processing guides

that are brieﬂy reviewed:

1. The tensor space is built and used to effectively

deﬁne and extract new HDR image features. The

tensor space is obtained by using tensor decom-

position to maintain three sets of HDR, assessing

HDR color image quality accurately and the struc-

ture of HDR image information;

2. Furthermore, in the ﬁrst feature map, learning

manifolds are used to ﬁnd inherent high dimen-

sional data geometric structures in low dimen-

sional manifolds. It contains primary energy and

important information about structural features on

the image;

3. In addition, in the ﬁrst map features the ﬁrst

extracted manifold structure multi-scale. While

multi-scale contrast features are extracted for

maps of the second and third features of HDR im-

ages, they reﬂect contrast information felt in detail

from the HDR image.

The extracted features were aggregated after per-

forming the above process by Supporting Vectors Re-

gression (SVR) to obtain the objective quality score

for HDR images.

CONRIST 2019 - International Conferences on Information System and Technology

Figure 2: Our proposed research road map

Their results showed that the proposed method

is consistent with subjective data. For a certain

database, it even outperformed some of the full ref-

erence HDR IQA such as HDR-VDP-2.2 methods.

5 CONCLUSIONS

We have surveyed various HDR image quality as-

sessment in the literature and found that many have

focused on the development of FR and NR model.

Therefore, development on RR model is considered

necessary. In line with that argument, we have ini-

tiated research on the development of RR model for

HDR IQA, using a research roadmap presented in

Figure 2. Some of our preliminary results have also

been published in (I. P. Gunawan and Santoso, 2019a)

and (I. P. Gunawan and Santoso, 2019b).

ACKNOWLEDGEMENTS

The author would like to thank the Indonesian

Ministry of Research and Higher Education un-

der the contract No. 11/AKM/PNT/2019, and

Universitas Bakrie, Indonesia, under the con-

tract No. 086/SPK/LPP-UB/III/2019 and No.

141/SPK/LPPUB/III/2019 for the funding of the re-

search presented in this paper.

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