图像质量评估：自然与医学图像分析

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"Visual Quality Assessment for Natural and Medical Image(2018).pdf" 该资源是一本由Yong Ding编著的书籍，专注于视觉质量评估在自然图像和医疗图像中的应用。随着数字图像和视频采集、传输及显示技术的快速发展，人们对高质量图像的需求在日常生活和学术、工程领域日益增加。图像质量评估（IQA）在这个过程中扮演了关键角色，它是现代大规模图像和视频处理系统设计中的性能评估和监控工具，有助于系统功能的提升。书中的内容涵盖了对自然图像和医疗图像视觉质量评估的研究，这在当前受到了广泛的关注并取得了显著的进步。IQA的目标是客观、有效和高效地评价图像质量，它不仅对于保证用户体验至关重要，也是优化图像处理算法和系统的基石。尤其是在医疗图像领域，准确的质量评估能够确保诊断的准确性，从而直接影响到病人的治疗效果。本书可能详细讨论了各种IQA方法，包括主观评价和客观评价。主观评价通常基于人类观察者的感知，而客观评价则试图通过数学模型模拟人类视觉系统。书中可能涉及了传统的全参考（Full-Reference, FR）IQA方法，如PSNR（峰值信噪比）和SSIM（结构相似性指数），这些方法依赖于原始无损图像进行比较。此外，无参考（No-Reference, NR）和减参考（Reduced-Reference, RR）IQA方法也可能被讨论，它们在无法获取原始图像的情况下评估图像质量。作者可能还探讨了特定于自然和医疗图像的挑战，例如，医疗图像可能包含复杂的解剖结构和噪声，这需要特殊的IQA策略来处理。此外，书中可能还涵盖了深度学习和人工智能技术在IQA中的应用，这些现代技术能够学习和模仿人类视觉系统，以更精确地预测图像质量。 "Visual Quality Assessment for Natural and Medical Image(2018)" 是一本深入研究图像质量评估的著作，对于图像处理、计算机视觉和医疗成像领域的研究人员、工程师和学生来说，是一份宝贵的参考资料。它提供了理论基础、最新技术和实际应用案例，帮助读者理解和改善图像处理系统的性能，特别是在关键的医疗图像分析中。

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processed by different test conditions), viewing conditions, grading scale, and

timing of presentation. For example, Recommendation (ITU-R) BT.50 0 (ITU 2012)

offers detailed guidelines for conducting the subjective television image quality

assessment experiments. Different types of subjective methods for experiment have

been developed on various public image quality databases. These subjective

methods usually contain single-stimulus-based methods and double-stimulus-based

methods. Wit h respect to single stimulus-based methods, the test subjects are only

shown test images with different distortions, in the absence of reference images. Of

course, in some conditions, a hidden reference image may be contained in the test

images; however, the evaluation is just based on the no-reference quality scores of

the test subjects. In comparison, double stimulus-based methods mean that the

subjects are shown test images along with their corresponding references. The

results of the subjective image quality assessment experiments are the quality scores

provided by the test subjects, which are employed to calculate the mean opinion

score (MOS), differential mean opinion score (DMOS), and another statistics

information. In general, the computed values of MOS and DMOS denote the

ground-truth information to develop the objective image quality assessment

algorithms.

2.2 Subjective Ratings

For subjective image quality assessment method, human subjects are required to

assess the image quality under test conditions. Subjective evaluation method is the

most reliable approach to determine the quality of real images. There are numerous

various methodologies and rules for designing and de ﬁning the subjective ratings.

In the next, several typical subjective rating methods will be introduced.

2.2.1 Participants—Subjects

People are usually accustomed to estimate objects from various aspects they

encounter. It is proved that judgements made by various people tend to be very

close (Shahid et al. 2014). With respect to quality of images, people especially

reach an agreement on the visual perception information such as the brightness,

contrast sensitivity, and visual masking of the images, which is because the

peripheral senses of different people are very similar. Meanwhile, this opinion on

visual perception information is uninhibited for people having their own opinions

about more cognitively related to the same things. On the one hand, everyone has

their own aesthetical quality of same things. On the other hand, many various often

personal reason s lead to consideration that some images can better give the essence

of things. In order to eliminate the deviation and produce reliable results, numbers

of people should be participated in the experiment. The researchers usually

6 2 Subjective Ratings and Image Quality Databases

accepted that 16–24 participants will be able to offer a reliable statistical result. In

general, the participants can be classiﬁed into expe rts and nonexperts according to

their experience in the image ﬁeld. All participants should be screened for visual

defects, such as visual acuity, color blindness, or color weakness (Wu and Rao

2006).

(1) Experts

Experts usually represent the researc hers with rich experience in the image ﬁeld,

such as image evaluation, image generation, image distribution, image compres-

sion, and image designing systems. The experts have a special point of view when

they observe the images. Employing the experts can become a quick test way, and

the experts know what they are working. However, some experts cannot free

themselves from their own mode of thinking. Experiments from the experts can be

beneﬁcial for the development of the image processing algorithms; however, it may

be bad for generalization to the common people. It is an advantage for using experts

that they are proﬁcient in offering the technical performance assessments of images.

In addition, the experts perform well in free form viewing.

(2) Nonexperts

Nonexperts usually denote the common people, domestic consumer, or someone

who will use the product. Sometimes nonexperts can ﬁnd the artifacts contained in

the images that the expert might miss. When the observation images are structured

properly, the nonexperts usually can provide the satisfactory experimental results.

When selecting the subjects, we should make great efforts to select a representative

sample, because the age, gender, and other factors may affect the experiment

results.

2.2.2 Experimental Design

There is a potential possibility for every variable in the test to impact the results of

image quality evaluation. It is important to consider variety of situations and

multiple types of images when designing the experiment.

For subjective image quality assessment, the source signal is very important

because it provides the reference image directly and is the input of the test under

examination. The stability of the experimental results will be directly affected if

there is a ﬂaw in the reference image. Digitally stored images are the ideal source

signal because they are reproducible and can be exchanged between the different

laboratories.

When conducting a subjective image quality assessment experiment, some

factors should be controlled, which contains carefully considering the number of the

images and the distortion types of images. It should be mentioned that an experi-

menter should complete these materials in ninety minutes. The sessions are gen-

erally divided into thirty minutes sections. In addition, the experimental structure

2.2 Subjective Ratings 7

will be affecte d by the time duration. A typical presentation diagram is shown in

Fig. 2.1, where the training images show the range and types of the distortions. It

should be mentioned that the images which are demonstrated to the subjects in the

training images time are illust rating images other than those employed in the test.

The goal of the training images is to make the subjects familiar with the experi-

mental environment. Moreover, in order to make the opinion of the subjects more

stable, ﬁve “dummy presentations” should be introduced at the beginning of the

experiment. The experimental data of these ﬁve presentations is not taken into

account in the experimental results.

2.2.3 Presentation of the Result

The experiments will generate distributions of integer value, e.g., between 1 and 5

or between 0 and 100. There will be some little variations in all distributions

because of the different judgements between the subjects and the effect of the

conditions. However, the results of the experiments are generally reported in the

form of mean opinion scores (MOS) or differential mean opinion scores (DMOS).

Therefore, the raw data from the trial should be processed.

An experi ment will contain numbers of presentations P. In addition, numbers of

conditions C will be included in one presentation. Moreover, numbers of images

I will be applied in a test condition. Under some circumstances, each combination

of test image and test condition can be repeated many times R.

Mean score MO S

ickr

is one of the most important embodiments of the experi-

mental results, which can be calculated by the following:

MOS

icr

k¼ 1

ickr

ð2:1Þ

where S

ickr

denotes the score of subject k for test condition c, image i, repetition

r, and N is the number of subjects. Overall mean scores can be computed by the

same way.

Training image(s)

Break(to allow time to

answer questions from

subjects)

Stabilizing

image(s)(results for

these items are not

processed)

Main part of test session

dummy presentation

Fig. 2.1 Presentation diagram of test session

8 2 Subjective Ratings and Image Quality Databases

Mean opinion score should have an associated interval when representing the

experimental results. Ninety-ﬁve percentage conﬁdence interval is usually used,

which is calculated by:

MOS

icr

 d

icr

; MOS

icr

þd

icr

½ð2:2Þ

icr

¼ 1.96

icr

ﬃﬃﬃﬃ

ð2:3Þ

icr

ﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃﬃ

k¼ 1

MOS

icr

 S

icrk

ðÞ

N  1ðÞ

ð2:4Þ

A raw data is taken as an outlier if the data is outside the conﬁdence interval. For

any session, if a number of values of an observer are outliers, all quality assess-

ments of the observer will be rejected from the obtained raw data. The algorithm of

outlier will be run twice.

In order to compute the DMOS, ﬁrstly, the raw scores should be converted into

quality difference scores, as shown in the following

¼ S

mref ðnÞ

 S

ð2:5Þ

where S

denotes the score of subject m, image n, and S

mref(n)

is the score of the

reference image corresponding to the impaired image n, which is obtained by the

subject m. Then, after the reprocessing of outlier removal and experimenter rejection

in the same way as above, the difference scores are converted into Z scores,

¼ d

 d



ð2:6Þ

where



denotes the mean difference score of all images rated by the subject m and

represents the standard deviation. Then, for the nth image, the Z scores z

are

obtained by calculating the average of z

across all subjects. By minimizing the

error between the DMOS z

ðÞand DMOS

which is obtained from the realignment

study, we can learn two values p

and p

. For acquiring the DMOS for the whole

database, a linear mappi ng between Z scores and DMOS is assumed, and the linear

mapping is given as follows:

DMOS zðÞ¼p

z þp

ð2:7Þ

2.2.4 Test Methods

The test methods introduced in this section are internationally recognized and

employed by the private enterprise, public sector, and standards organizations to

carry out the subjective tests (Choe et al. 1999 ). It is must careful about each

2.2 Subjective Ratings 9

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