A Full Reference Based Objective Image Quality Assessment

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ISSN (Print) : 2278-8948, Volume-2, Issue-6, 2013

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A Full Reference Based Objective Image Quality Assessment

Mayuresh Gulame, K. R. Joshi & Kamthe R. S. P.E.S Modern College of Engineering, Pune -5

E-mail : [email protected], [email protected] [email protected]

Abstract – Measurement of image quality is important for many image processing applications. This paper mainly aims to study the performance of objective assessment methods of image quality. The interest in objective image quality assessment (IQA) has been growing at an accelerated pace over the past decade. They are categorized in to pixel difference based, correlation based, edge based measures, spectral based measure, distance based measures etc. In this paper objective IQA measures like mean square error (MSE) and peak signal to noise ratio (PSNR), Signal to noise ratio, maximum difference, Normalised absolute error, Structural content,laplacianMSE, universal quality index (UQA), SSIM, etc.which is based on full reference image quality assessment are explained Moreover various distance measures and their effect on MRI image to measure quality are explained. And these image quality measures which are used for image signals with extended dimensions creates many challenging research problems, which include video, color, multispectrum, hyper spectrum, and stereo, medical imaging applications.

Keywords – image quality assessment (IQA), structural similarity index (SSIM), mean squared error (MSE), peak signal to noise ratio (PSNR).

I. INTRODUCTION

Digital images are subject to a wide variety of distortions during acquisition, processing, storage, transmission and reproduction, any of which may result in a degradation of visual quality. So, measurement of image quality is very important to numerous image processing applications. The main function of human eye is to extract structural information from the viewing field, and the HVS (human visual system) is highly adapted for this purpose. Therefore, for the applications in which images are ultimately to be viewed by human beings, the only “correct” method of quantifying visual image quality is through subjective evaluation. In practice, however, subjective evaluation is usually too inconvenient, time-consuming and expensive. In recent years, a lot of efforts have been made to develop objective image quality metrics that correlate with

perceived quality. Image quality assessment and comparison metrics play an important role in various graphics oriented applications. They can be used to monitor image quality for quality control systems, they can be employed to benchmark image processing algorithms, and they can be embedded into the rendering algorithms to optimize their performances and parameter settings. In general, measurement of image quality usually can be classified into two categories, which are subjective and objective quality measurements.

1. Subjective measurement

A number of observers are selected, tested for their visual capabilities, shown a series of test scenes and asked to score the quality of the scenes. It is the only

“correct” method of quantifying visual image quality.

However, subjective evaluation is usually too inconvenient, time-consuming and expensive.

2. Objective measurement

These are automatic algorithms for quality assessment that could analysis images and report their quality without human involvement. Such methods could eliminate the need for expensive subjective studies. Objective image quality metrics can be classified according to the availability of an original (distortion-free) image, with which the distorted image is to be compared. Most existing approaches are known as: -

(i) Full-reference: meaning that a complete reference image is assumed to be known.

(ii) No-reference: In many practical applications, however, the reference image is not available, and a no-reference or “blind”quality assessment approach is desirable.

(iii) Reduced-reference: In a third type of method, the reference image is only partially available, in the form of a set of extracted features made available as side information to help evaluate the quality of the

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14 distorted image. The work in this paper is based on the design of full-reference category[2]

II. NEED OF QUALITY MEASURE

As we know the importance of quality of images and videos and the associated cost-quality balance, the obvious question that arises is why we need to measure quality. The answer is simple and could be illustrated by a few examples. If a designer is designing this high-end television, and wants to know what the quality-cost curve looks like, he obviously needs a mechanism for measuring the quality of the output video when his design is running at certain configuration costing a certain resource. In another scenario, a designer of a medical imaging device may want to decide which of the two alternative X-ray devices gives better results. He too needs a way of scientifically comparing the quality of the two systems. Basically, quality assessment algorithms are needed for mainly three types of applications:

1. For optimization purpose, where one maximize quality at a given cost.

2. For comparative analysis between different alternatives.

3. For quality monitoring in real-time applications.[12]

III. BASIC IQA SYSTEM Input Output

Fig. 1: Basic IQA Flow

A block diagram of a basic IQA system is presented in Figure1. According to the availability of a reference image, objective IQA metrics can be classified as full reference (FR), no-reference (NR) and reduced- reference (RR) methods. In case of FR methods, where the original “distortion free” image is known as the reference image. As figure shows, where depending on the application, FR, RR, or NR IQA measures may be employed .[2]For example, in the case of image enhancement, an NR method may be employed and only the image created at the output end is needed for IQA

computation. In image coding applications, an FR IQA measure could be used that requires both decoded image from the output end and the original reference image from the input (which is linked through the dashed line).In the second approach of IQA-based optimal design, the objective IQA measure goes into the core of the image processing algorithm. Image quality measures are figures of merit used for the evaluation of imaging systems or of coding/ processing techniques. In this paper considering several image quality metrics and study their statistical behavior when measuring various compression and/or sensor artifacts good objective quality measure should reflect the distortion on the image well due to, for example, blurring, noise, compression, and sensor inadequacy. [2]

Noise Based Full reference Image quality assessment:

The ability to quantify the visual quality of an image in a manner that agrees with human vision is a crucial step for any system that processes consumer images. Over the past several decades, research on this front has given rise to a variety of computational methods of image quality assessment. So-called full- reference quality assessment methods take as input an original image and a distorted version of that image, and yield as output a prediction of the visual quality of the distorted image relative to the original image. The effectiveness of an image quality assessment method is gauged by examining how well the method can predict ground-truth, human-supplied quality ratings obtained via subjective testing.

IV. FULL REFERENCE OBJECTIVE QUALITY MEASUREMENT

Definition: x(m,n) denotes the samples of original image, x ^(m, n) denotes the samples of compressed image. M and N are number of pixels in row and column directions, respectively.

Mean Square Error (MSE)

The simplest of image quality measurement is Mean Square Error (MSE). The large value of MSE means that image is poor quality. MSE is defined as follow:

(1) Mean Average Error (MAE)

The large value of Mean Average Error (MAE) means that image is poor quality. MAE is defined as follow:

Image Processing

System

Updating Algorithm

IQA Evaluation

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(2) Peak Signal to Noise Ratio (PSNR)

The small value of Peak Signal to Noise Ratio (PSNR) means that image is poor quality. PSNR is defined as follow:

(3) Structural Content (SC)

The large value of Structural Content (SC) means that image is poor quality. SC is defined as follow:

(4) Maximum Difference (MD)

The large value of Maximum Difference (MD) means that image is poor quality. MD is defined as follow:

(5) Normalized Absolute Error (NAE)

The large value of Normalized Absolute Error (NAE) means that image is poor quality. NAE is defined as follow:[4]

(6) Laplacian Mean Square Error (LMSE)

This measure is based on the importance of edges measurement. The large value of Laplacian Mean Square Error (LMSE) means that image is poor quality.

LMSE is defined as follow: [4]

(7) where L(m,n) is laplacian operator:

L(x(m,n))=

x(m+1,n)+x(m−1,n)+x(m,n+1)+x(m,n−1)−4x(m,n)

Structural similarity index(SSIM)

Given the obvious limitations of the mean squared error, propose a more intelligent solution to the problem of image quality assessment. Made up of three terms, the structural similarity (SSIM) index estimates the

visual impact of shifts in image luminance, changes in photograph contrast, as well as any other remaining errors, collectively identified as structural changes. The metric is based on a top-down assumption that the HVS is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity should be a good approximation of perceived image quality For original and coded signals x and y, respectively, the SSIM index is defined as:

(8) The luminance, contrast and structural components of the index are defined individually

as where µx and µy represent the means of the original and coded images, respectively, µx and µy represent the standard deviations of each of the signals and σxy is the covariance of the two images As a means of dealing with the situations in which the denominators are close to zero, the constants C1, C2 and C3 are introduced. For an 8-bit grayscale image composed of L = 2^8 = 256 gray-levels, C1 = (K1*L) ^2, C2 = (K2*L) ^2 and C3 = C2=2, where K1 = 0.01 and K2 = 0.03. When C1 = C2

= 0, the metric is reduced to the universal quality index.

[6][7]

V. FULL REFERENCES DISTANCE BASED MEASURES

It is expected that the distance between feature vectors of training and testing image from the same class should be minimum and from the different class should be maximum. In this paper we compare different distance measures. In this paper, briefly elaborate six commonly used distance measures. An important family of distance measures is formed by Minkowski distances If P = (P1, P2,…,Pn) and Q = (Q1,Q2,…,Qn) are two feature vectors

Euclidean Distance

Euclid stated that the shortest distance between two points is a length of a straight line joining those two points and the Eq. is shown below:

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(9) Manhattan Distance

It is a distance between two points measured along axes at right angles. It is also known as rectilinear distance or city block distance and shown below:

(10) Chebyshev Distance

In mathematics, Chebyshev distance, Maximum metric, is a metric defined on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension and shown below:

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Bray Curtis Distance

The Bray–Curtis dissimilarity, named after J. Roger Bray and John T. Curtis is a statistic used to quantify the compositional dissimilarity between two different sites, based on counts at each site and shown below:

D (P,Q)=

(12) Canberra Distance

The Canberra distance is a metric function often used for data scattered around an origin. The distinction is that the absolute difference between the variables of the two objects is divided by the sum of the absolute variable values prior to summing.

(13) Cosine Correlation Distance

Cosine similarity is a measure of similarity between two vectors by measuring the cosine of the angle between them.

(14) VI. RESULTS

Results Obtained: (Full references quality metric for cameraman image):

Table 1 Quality metric with different noise

Table 2 Expected value of quality metric Table 1.gives computed values of the objective quality measures obtained. Normalized correlation gives closeness between the original and decoded image and is obtained as 0.991, 0.994, 0.1.001,for salt and pepper(0.02), Speckle Noise with v=0.04, Gaussian noise with 0 mean 0.01 variance, Gaussian noise with 0 mean 0.01 variance for standard cameraman image respectively. This value tends to 1 if the difference between the images is zero.

The computed values Mean absolute error is 1.102,10.33,9.21 for salt and pepper(0.02), Speckle Noise with v=0.04, Gaussian noise with 0 mean 0.01 variance Gaussian noise with 0 mean 0.01 variance for standard cameraman image respectively.

The structural content is a global measure, which compares the total weight of the compressed image and original image, is 1.0093,1.0045,1.035 for for salt and pepper(0.02), Speckle Noise with v=0.04, Gaussian noise with 0 mean 0.01 variance Gaussian noise with 0 mean 0.01 variance for standard cameraman image respectively.

Results in Images with different Noise:

Fig. 2 : Original Image Fig.3 :Speckle noise (v=0.04)

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Fig. 4: Salt and pepper (0.02) Fig.5: Gaussian noise v= (0.01) Results Obtained: (Full references quality metric for Enhanced MRI image):

Table 3 Quality metric for MRI image

(a) (b) Fig.6 (a) Original Enhanced MRI Image

(b) Speckle Noise with v=0.04

Results Obtained: (Full references quality metric for Enhanced MRI image) For distance based measures:

Table 4 Quality metric for MRI image for distance based measures

The performance of various distance criteria such as Minkowski Distances (Euclidean, Manhattan, Chebyshev), BrayCurtis, Canberra and Cosine similarity is tested for MRI image .From the results it can be concluded that Chebyshev distance measure performance is almost constant for any type of noise.

Manhattan and Bray-Curtis distance measure give overall better performance compared to other distances metric. Canberra distance measure does not give good

Performance for medical image for different type of noise . Ultimately for medical images Manhattan and Bray-Curtis distance measure and cosine correlation distance should measures for quality measures of medical images.

VII. CONCLUSION

These proposed works, collectively a comprehensive set of image quality measures and categorize using statistical tools. It is able to classify based on their sensitivity to different types of distortions. In this, effect of full references quality metric on Cameraman image and MRI image and how that quality metric is useful for assessment of quality is shown. Moreover effect of distance based metric on medical image is shown. Out of which Manhattan and Bray-Curtis distance measure and cosine correlation distance should measures for quality measures of medical images.

VIII. REFERENCES

[1] A New Method for Color Image Quality Assessment, Niveditta Thakur Chandigarh February 2011.

[2] IEEE signal processing magazine Zhou Wang University of Waterloo, Canada NOVEMBER 2011,

[3] Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P.

Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Trans.

[4] Objective Performance of Compressed Image Quality Assessments Ratchakit Sakuldee, and Somkait Udomhunsakul 2007

[5] Thung, Kim-Han, Raveendran, Paramesran..

Asurvey of image quality measures. In Proceedings ofInternational Conference for Technical Postgraduates 2009

[6] SSIM Image Quality Metric for Denoised Images.Peter ndajah,hisakazu kikuchi,masahiro Yukawa,hidenori September 2010.

[7] Image Quality Assessment and Human Visual System Xinbo Gao Chinese Academy of Sciences, Xi'an 710119, Shaanxi, China.

[8] Multi-scale structural similarity for image quality assessment zhou wang1, eero p. simoncelli and alan c. bovik, center for neural sci. New York.

[9] Image enhancement quality metricsAndrey Nasonov, Andrey Krylov, Moscow State University, Moscow, Russia 2010.

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18 [10] The performance of fractal image compression on

different imaging modalities using objective quality Measures SUMATHI POOBAL Professor, KCG College of Technology, Chennai, India Jan 2011.

[11] Adaptive Algorithm for Image Denoising Based on Curvelet Threshold IJCSNS International Journal of Computer Science and Network Security, VOL.10 No.1, January 2010.

[12] Image Quality Assessment Techniques pn Spatial Domain .C.Sasi varnan, A.Jagan, Jaspreet Kaur, Divya Jyoti, Dr.D.S.Rao September 2011.

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