MOS Prediction of SPIHT Medical Images Using Objective Quality

Parameters

Basant Kumar, S.P. Singh, Anand Mohan and Harsh Vikram Singh

Department of Electronics Engineering, Institute of Technology, Banaras Hindu

University

Varanasi-221005, India

Email: singhbasant@yahoo.com

Abstract

Correlating objective and subjective quality assessment

parameters of compressed digital medical images has

been an open challenging problem in tele-radiology.

Establishing this correlation is crucial in determining the

upper limit of image compression threshold for

preserving diagnostically relevant information based on

mean opinion score (MOS).

This paper presents a suitable method for finding

correlation between PSNR and Structural Similarity

(SSIM) index objective image quality parameters with

subjective MOS for SPIHT [4] compressed medical

images based on six independent observers. The

suggested method can be potentially used for deciding

upper compression thresholds for medical images. It is

found that correlation coefficient (CC) between the PSNR

and MOS for CT scan and MRI images are 0.979 and

0.960 respectively whereas their corresponding values

are 0.868 and 0.955 considering SSIM. Further, MOS

prediction models have been proposed considering PSNR

and SSIM which closely match with the subjective MOS.

1. Introduction

Tele-radiology involves transmission of compressed

medical images over a wireless / wired communication

network to reduce the channel bandwidth requirements and transmission time. However, it reduces the fidelity of

the reproduced medical image, especially when the

images are compressed at lower bit rates using lossy

compression techniques. As the fidelity of reconstructed

images crucially affects clinical diagnosis therefore

presence of any visual artifact due to compression is

undesirable because it may hinder diagnostic conclusions

and lead to serious lapses. This necessitates preserving

diagnostically relevant information of the compressed

medical images. Therefore a lossy medical image

compression can be used only to the extent it does not compromise with the diagnostically relevant image details

and the image degradation is not perceptible. Currently,

Wavelet coding has proved very effective for achieving

higher compression of medical images than JPEG

algorithm with comparable computational efficiency [1]-

[2]. This is because wavelet coding avoids presence of blocking artifact because no image partitioning is required

and it supports progressive transmission capability useful

for telemedicine.

The set partitioning in hierarchical tree (SPIHT)

compression algorithm [4] is highly refined version of the

Embedded Zerotree Wavelet (EZW) algorithm proposed

by Shapiro [3]. It is widely used for image compression

and provides a basic standard of comparison for all

subsequent algorithms. Image compression for

telemedicine requires objective quality assessment (QA)

based on peak signal to noise ratio (PSNR) and mean squared error (MSE) widely used parameters; and its

correlation with subjective human evaluation expressed as

MOS. However, these objective quality parameters do not

correlate well with perceived quality measurement [6]

which is important to determine the upper compression

threshold for retaining the subjective perceptual quality of

the received image.

As a result, number of studies have been carried out to

improve computation methods for quality assessment

(QA) of reproduced images generated using wavelet

compressed images [5]. Since human visual system

(HVS) is highly adaptive to extract structural information from the viewing field [7] and thus measurement of its

change provides good approximation to perceived image

distortion. For example, the Structural Similarity Index

(SSIM) is a measure of structural similarity quality which

uses contrast sensitive function approach of HVS to

estimate the quality of the compressed image.

This paper describes a method for finding correlation

between objective image quality parameters (PSNR and

SSIM) and subjective MOS for SPIHT compressed CT

scan and MRI images. Identification of the objective

quality parameter having greater influence on MOS has been carried out based on correlations and MOS

prediction models have been proposed considering PSNR

and SSIM which closely matches with the subjective

MOS. MATLAB simulation results indicate that PSNR

has higher impact (correlation 0.979) on MOS for CT

scan than its effect on MRI (correlation 0.960).

2009 International Conference on Signal Processing Systems

978-0-7695-3654-5/09 $25.00 © 2009 IEEE

DOI 10.1109/ICSPS.2009.34

219

Contrarily, SSIM has lesser effect on MOS of CT scan

(correlation 0.868) as compared to its influence on MOS

for MRI images (correlation 0.955). Thus PSNR and

SSIM are respectively more suitable for MOS estimation

of CT scan and MRI images.

Section 2 describes SPIHT compression algorithm for medical images followed brief description on metrics of

objective and subjective quality in section 3. Section 4

presents determination of correlation between PSNR and

SSIM with subjective quality metrics i.e. MOS for SPIHT

compressed CT scan and MRI images at different

compression bit rates along with determination of degree

of influence of the objective quality parameter on MOS

prediction for the images based on computed correlation

coefficients. It also contains the proposed MOS prediction

models based on correlation coefficients and section V

contains conclusion.

2. SPIHT compression

The SPIHT compression of medical images is similar

to conventional wavelet coding but it differs in encoding

of the wavelet coefficients [4, 8]. SPIHT coding exploits

the wavelet transform hierarchical structure using tree-

based organization of the coefficients performs, partial

ordering of the transformed coefficients by magnitude,

and uses ordered bit plane transmission of refinement bits

for the coefficient values. This generates a compressed bit

stream such that most significant coefficients are

transmitted first. The values of all coefficients are

progressively refined and the relationship between coefficients representing the same location at different

scales is fully exploited for compression efficiency. Fig. 1

illustrates the inter-band spatial dependencies which are

captured in the form of parent-offspring relationships

where the arrows point from the parent node to its 4

children at the same relative location in the sub-band

decomposition structure. With the exception of the

coarsest sub-band and the finest sub-bands, each wavelet

coefficient at the kth level of decomposition is spatially

correlated to 4 child coefficients at level k-1 in the form

of 2x2 blocks of adjacent pixels. For an insignificant parent coefficient with respect to a particular threshold its

all children would be most likely insignificant. Similarly

significant coefficients in the finer sub-band most likely

correspond to a significant parent in the coarser sub-band.

Thus only the parent’s significance needs to be coded

because the children’s contribution to the pixel can be

inferred from the parents.

3. Quality Assessment Metrics

One of the main problems hindering further

development of compression schemes is non-availability

of accurate image quality prediction metrics. The most

commonly used metrics are PSNR / MSE of image. But

unfortunately they neither match well with perceived

visual quality nor provide means for accurate prediction

of visual quality across a set of images with varying

content such as edges, textured regions, and large

luminance variations. However, numerous studies have been carried out over last three decades to develop quality

assessment methods exploiting known characteristics of

the human visual system (HVS) [6, 7, 9]. A different

approach for

Fig.1. Parent-offspring dependencies in the spatial-orientation tree

objective image quality assessment which uses Structural

Similarity (SSIM) index [7] has proved potentially useful

over traditional error sensitivity based approaches.

Although subjective and diagnostic evaluations are even

more suitable for such applications [10] but this can’t be

incorporated into automatic systems. Therefore objective

quality measures are often used since they are easy to

compute and are applicable to all kinds of images regardless of the application.

3.1. Objective measures

The widely used objective quality measures of an

image are MSE, PSNR [10], and SSIM index. The MSE is defined as the mean of the square of the difference

between the original and reconstructed pixels, x and x′, of an image is expressed by equation (1).

[ ]2

1 1

'

..

1∑∑

= =

−=M

i

N

j

jiji xxMN

MSE (1)

where x and x′ are the intensities of original and reconstructed pixels, respectively and M x N is the image

size.

PSNR in decibels (dB) can be evaluated by equation (2).

dBMSE

PPSNR

=

2

10log10 (2)

220

where P is the maximum possible pixel value, e.g. 255 for

an 8-bit grey-level image.

The objective quality measure exploits HVS

properties. An effective method for objective image

quality assessment was suggested by Zhou Wang et al. [7]

based on determination of variation in SSIM by comparing local patterns of pixel intensities that have

been normalized for luminance and contrast; and

assuming that HVS is highly adaptive to extract structural

information from the viewing field. Considering a and b

as two non-negative image signals if one of the signals is

assumed to have perfect quality then the similarity

measure can be used as a quantitative parameter for the

quality of the second signal as compared to original

signal. Therefore SSIM can be computed using equation

(3).

))((

)2)(2(),(

2

22

1

22

21

CC

CCbaSSIM

baba

abba

++++

++=

σσµµ

σµµ (3)

where µa and µb are the mean intensities and σa and σb are the standard deviations of a and b respectively and C1

and C2 are constants. In discrete form σab can be estimated as:

∑=

−−−

=N

i

biaiab baN

1

))((1

1µµσ (4)

The image quality assessment is achieved by locally

applying the SSIM index to compute local statistics

within a local w x w square window which moves pixel-

by-pixel over the entire image. At each step, the local

statistics and SSIM index are calculated and the overall

quality measure of the entire image is determined using

mean SSIM (MSSIM) index given by equation (5)

∑=

=M

i

ii baSSIMM

BAMSSIM1

, )(1

),( (5)

where A and B are the original and reconstructed images

respectively; ai and bi are the image contents at the ith

local window; and M is the number of local windows of

the image.

3.2. Subjective measures [10-12]

Subjective evaluation by viewers with normal or

corrected to normal eye sight is still commonly used in

measuring image quality. In case of medical images, a

radiologist can judge the quality of the medical image by inspecting the loss of diagnostic information in the image.

Although the perceived image quality has been defined by

CCIR using 5-point scale as bad, poor, fair, good and

excellent [13] but numerical 10 point scale [14] is much

more convenient. This is because these scales are linear

and can be easily adapted to specific range of image

qualities and quality assessment is achieved by taking

opinions of the observer’s rating of overall perceived

quality of each image on 1-10 scale. Finally, an average

score is computed to obtain the mean opinion score

(MOS) for a specific image using equation (6).

∑=

=n

i

jiSn

jMOS1

),(1

)( (6)

where n denotes the number of observers and S (i, j) is the

score given by the ith observer to image j. We have

considered six observers to compute MOS of CT scan and

MRI images under the present study.

4. Performance evaluation and discussion

Experiments were carried out considering CT scan and

MRI medical images of size 512 x 512 each with 8-bit

grey levels available in reference [15]. The images were

compressed using SPIHT algorithm. The MSE, PSNR,

and SSIM index objective quality assessment parameters

have been evaluated by varying bit rates (bit per pixel) in

the range 0.1 to 2.0. The computed values of these

parameters along with MOS values for CT scan and MRI

images are shown in tables 1 and 2 respectively. The six

observers for determining MOS had normal or corrected-to-normal vision and were non-experts. PSNR, SSIM,

and MOS values in tables 1 and 2 have been used for

computation of correlation coefficients (CC) to determine

the degree of influence of objective quality parameters on

MOS as shown in table 3. The variations in the PSNR and

SSIM as function bit rates for CT scan and MRI images

are given in figures 2 and 3 respectively. The example

reconstructed CT scan and MRI images corresponding to

0.1, 0.5, and 1.0 bit rates are shown in figures 4 and 5.

The computed values of CC between PSNR / SSIM and

MOS for CT scan and MRI images are given in table 3. Referring table 3 it is found that for CT scan image their

respective values are 0.979 and 0.868; and for MRI the

corresponding values are 0.960 and 0.955. Analyzing the

CC values in tables 3 it is evident that PSNR offers better

correlation with the MOS as compared to SSIM, however,

it is necessary to consider larger set of images and

observers to draw a general conclusion. To establish

relation between subjective MOS and its model predicted

value the scatter plots have been generated for variations

in MOS as function of PSNR and SSIM as shown in figs.

6 and 7 respectively using third order polynomial fit

function. These non-linear regression functions have been used to transform the set of model outputs into a set of

predicted MOS values. Subsequently the CC values are

computed between the predicted and subjective MOS

values which are presented in the Table4. It can be

observed from the table 4 that correlation between

subjective MOS and its model predicted value is better for

PSNR than SSIM. Thus PSNR is more suitable for

predicting model based MOS value at a given image

compression.

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TABLE 1. COMPARISON OF SPIHT PERFORMANCE FOR CT IMAGE AT DIFFERENT BIT

RATES

TABLE 2. COMPARISON OF SPIHT PERFORMANCE FOR MRI IMAGE AT DIFFERENT BIT RATES

TABLE 3. CORRELATION COEFFICIENTS (CC) BETWEEN PSNR/SSIM AND MOS FOR

CT SCAN AND MRI IMAGES

TABLE 4. CORRELATION COEFFICIENTS (CC) BETWEEN MODEL PREDICTED MOS VALUE AND SUBJECTIVE MOS

Fig. 2. Bit rate versus PSNR for medical images

Fig. 3. Bit rate versus SSIM for medical images

Fig. 4. (i) Original CT scan, (ii) - (iv) CT scan reconstructed at 0.1, 0.5

and 1.0 bpp respectively

Fig. 5. (i) Original MRI image, (ii) - (iv) MRI image reconstructed at

0.1, 0.5 and 1.0 bpp respectively

(a)

222

(b)

Fig. 6. Scatter plots of subjective mean opinion score (MOS) versus

PSNR model prediction for (a) CT scan (b) MRI image

(a)

(b)

Fig 7 Scatter plots of subjective mean opinion score (MOS) versus SSIM

model prediction for (a) CT scan (b) MRI image

5. Conclusion

This paper described a method for establishing correlation

between objective and subjective image quality

parameters for SPIHT compressed medical images along

with a model for predicting MOS values using objective

PSNR and SSIM quality parameters. It shown that the

proposed model for MOS prediction well matches with observer’s based MOS at different bit rates in the range

0.1 to 2.0 for the considered CT scan and MRI images.

The suggested model can be potentially useful in

determining upper image compression thresholds in

telemedicine applications.

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