Image Quality Assessment
With the rapid growth in Internet technologies and the enormous use of
multimedia content for different applications, from education to entertain-
ment, paramount importance is given to the use of images and videos. In
order to save space and transfer in less time, these images and videos are
compressed. Due to compression, artifacts are generated, which degrades
the quality of the images. Research is still going on to increase the compres-
sion ratio by degrading quality to a small extent. The extent to which quality
is degraded should be analyzed for further performance improvement of
the compression algorithm. The full-reference quality metrics such as peak
signal-to-noise ratio, mean square error, structural similarity, and image
delity are explained. If the reference image is not available, the technique
used to determine quality is discussed. The image quality assessment tech-
nique based on information from the reference image (reduced reference) is
also discussed.
7.1 Introduction
During the last three decades, because of advances in imaging technolo-
gies and the rapid growth of the Internet, digital images and videos are now
widely used for representing information in entertainment. We have studied
in previous chapters that due to the large size of images and videos, they
need to be compressed. As a result of the compression artifacts, the quality
of images degrades. The quality also suffers during acquisition, transmis-
sion, and reproduction and processing. So, image quality measurement has a
wide importance in many applications related to image processing. Efcient
and automatic objective quality evaluation using the development of quality
assessment algorithms is the main goal of quality assessment research. Also,
it should be consistent with subjective/human assessment. Identication
and quantication of image quality degradations by the algorithms for the
assessment are also necessary. This will be to update the performance of the
compression process or channel.
138 Image and Video Compression
Human beings are the nal recipients in many image-processing applica-
tions; images are assessed by subjects (observers). This method is based on
the mean of the opinion given by observers. Even though correct judgment
is possible, this method is fairly expensive and slow. This approach is called
subjective image quality assessment (IQA).
Predication of the perceived image quality accurately based on computa-
tional models is another way. The prediction is supposed to be in correlation
with subjective assessment. This approach is called an objective IQA.
Thus, image quality assessment techniques are classied mainly into (1)
subjective and (2) objective. Further objective quality assessment is classied
into the following:
1. Full reference (FR)—In this type of assessment, the original image
before processing (compression) is used as reference. The decom-
pressed or distorted image is compared with reference, typically
using distance criteria. This method is still used by the researchers
to evaluate performance of the compression scheme developed.
2. No reference (NR)In this approach, there is an image available as
reference. Certain artifacts/features in an image like blockiness and
ringing are measured and given to a well-trained prediction model
to get the quality score. The model uses subjective assessment dur-
ing training. The task is difcult but is drawing the attention of
many researchers to develop no reference quality metrics.
3. Reduced reference (RR)Certain features like edge information and
pixel values at a xed position are extracted and attached as side
information along with the compressed image. For assessment, the
features are compared and reduced so that a reference quality met-
ric can be developed. This approach can also be used for image qual-
ity improvement (repair).
Subjective IQA is discussed in the next sections, and all objective IQA
techniques are discussed in further sections along with MATLAB programs
and comparison between various quality metrics.
7.2 Subjective Image Quality Analysis
Subjective image quality analysis may be the only best method for quantify-
ing visual image quality. The mean opinion score (MOS) is a widely used
method for subjective assessment of image quality. In this method, ve
or more grade scales are used. Experts and nonexperts are asked to give
grades for images as 5 for “Excellent,” 4 for “very good,” 3 for “good,” 2 for
“bad,” and 1 for “very bad.” The average of the grades given to that image is

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