IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 13, NO. 4, APRIL 2004 1
Image Quality Assessment: From Error Visibility to
Structural Similarity
Zhou Wang, Member, IEEE, Alan C. Bovik, Fellow, IEEE
Hamid R. Sheikh, Student Member, IEEE, and Eero P. Simoncelli, Senior Member, IEEE
Abstract— Objective methods for assessing perceptual im-
age quality have traditionally attempted to quantify the vis-
ibility of errors between a distorted image and a reference
image using a variety of known properties of the human
visual system. Under the assumption that human visual
perception is highly adapted for extracting structural infor-
mation from a scene, we introduce an alternative framework
for quality assessment based on the degradation of struc-
tural information. As a specific example of this concept,
we develop a Structural Similarity Index and demonstrate
its promise through a set of intuitive examples, as well as
comparison to both subjective ratings and state-of-the-art
objective methods on a database of images compressed with
JPEG and JPEG2000.
1
Keywords— Error sensitivity, human visual system (HVS),
image coding, image quality assessment, JPEG, JPEG2000,
perceptual quality, structural information, structural simi-
larity (SSIM).
I. Introduction
Digital images are subject to a wide variety of distor-
tions during acquisition, pro cessing, compression, storage,
transmission and reproduction, any of which may result
in a degradation of visual quality. For applications in
which images are ultimately to be viewed by human be-
ings, the only “correct” method of quantifying visual im-
age quality is through subjective evaluation. In practice,
however, subjective evaluation is usually too inconvenient,
time-consuming and expensive. The goal of research in ob-
jective image quality assessment is to develop quantitative
measures that can automatically predict perceived image
quality.
An objective image quality metric can play a variety of
roles in image processing applications. First, it can be
used to dynamically monitor and adjust image quality. For
example, a network digital video server can examine the
quality of video being transmitted in order to control and
allocate streaming resources. Second, it can be used to
optimize algorithms and parameter settings of image pro-
cessing systems. For instance, in a visual communication
The work of Z. Wang and E. P. Simoncelli was supported by the
Howard Hughes Medical Institute. The work of A. C. Bovik and H.
R. Sheikh was supported by the National Science Foundation and the
Texas Advanced Research Program. Z. Wang and E. P. Simoncelli are
with the Howard Hughes Medical Institute, the Center for Neural Sci-
ence and the Courant Institute for Mathematical Sciences, New York
University, New York, NY 10012 USA (email: zhouwang@ieee.org;
eero.simoncelli@nyu.edu). A. C. Bovik and H. R. Sheikh are with the
Laboratory for Image and Video Engineering (LIVE), Department
of Electrical and Computer Engineering, The University of Texas
at Austin, Austin, TX 78712 USA (email: bovik@ece.utexas.edu;
hamid.sheikh@ieee.org).
1
A MatLab implementation of the proposed algorithm is available
online at http://www.cns.nyu.edu/~lcv/ssim/.
system, a quality metric can assist in the optimal design of
prefiltering and bit assignment algorithms at the encoder
and of optimal reconstruction, error concealment and post-
filtering algorithms at the decoder. Third, it can be used
to benchmark image processing systems and algorithms.
Objective image quality metrics can be classified accord-
ing to the availability of an original (distortion-free) image,
with which the distorted image is to be compared. Most
existing approaches are known as full-reference, meaning
that a complete reference image is assumed to be known. In
many practical applications, however, the reference image
is not available, and a no-reference or “blind” quality as-
sessment approach is desirable. 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 infor-
mation to help evaluate the quality of the distorted image.
This is referred to as reduced-reference quality assessment.
This paper focuses on full-reference image quality assess-
ment.
The simplest and most widely used full-reference quality
metric is the mean squared error (MSE), computed by aver-
aging the squared intensity differences of distorted and ref-
erence image pixels, along with the related quantity of peak
signal-to-noise ratio (PSNR). These are appealing because
they are simple to calculate, have clear physical meanings,
and are mathematically convenient in the context of opti-
mization. But they are not very well matched to perceived
visual quality (e.g., [1]–[9]). In the last three decades, a
great deal of effort has gone into the development of quality
assessment methods that take advantage of known charac-
teristics of the human visual system (HVS). The majority
of the proposed perceptual quality assessment models have
followed a strategy of modifying the MSE measure so that
errors are penalized in accordance with their visibility. Sec-
tion II summarizes this type of error-sensitivity approach
and discusses its difficulties and limitations. In Section III,
we describe a new paradigm for quality assessment, based
on the hypothesis that the HVS is highly adapted for ex-
tracting structural information. As a specific example, we
develop a measure of structural similarity that compares lo-
cal patterns of pixel intensities that have been normalized
for luminance and contrast. In Section IV, we compare the
test results of different quality assessment models against
a large set of subjective ratings gathered for a database of
344 images compressed with JPEG and JPEG2000.
