Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products. By applying postprocessing techniques on the incoming signal, they aim to enhance the quality level perceived by the viewer. These postprocessing techniques are usually applied globally over the whole image but may cause side effects, the visibility and annoyance of which differ with local content characteristics. To better understand and utilize this, a three-phase experiment was conducted where observers were asked to score images that had different levels of quality in their regions of interest and in the background areas. The results show that the region of interest has a greater effect on the overall quality of the image than the background. This effect increases with the increasing quality difference between the two regions. Based on the subjective data we propose a model to predict the overall quality of images with different quality levels in different regions. This model, which is constructed on empirical bases, can help craft weighted objective metrics that can better approximate subjective quality scores.
In current magnetic resonance (MR) imaging systems, design choices are confronted with a trade-off between structured
(i.e. artifacts) and unstructured noise. The impact of both types of noise on perceived image quality, however, is so far
unknown, while this knowledge would be highly beneficial for further improvement of MR imaging systems. In this
paper, we investigate how ghosting artifacts (i.e. structured noise) and random noise, applied at the same energy level in
the distortion, affect the perceived quality of MR images. To this end, a perception experiment is conducted with human
observers rating the quality of a set of images, distorted with various levels of ghosting and noise. To also understand the
influence of professional expertise on the image quality assessment task, two groups of observers with different levels of
medical imaging experience participated in the experiment: one group contained fifteen clinical scientists or application
specialists, and the other group contained eighteen naïve observers. Experimental results indicate that experts and naïve
observers differently assess the quality of MR images degraded with ghosting/noise. Naïve observers consistently rate
images degraded with ghosting higher than images degraded with noise, independent of the energy level of the
distortion, and of the image content. For experts, the relative impact of ghosting and noise on perceived quality tends to
depend on the energy level of the distortion and on the image content, but overall the energy of the distortion is a
promising metric to predict perceived image quality.
Reliably assessing overall quality of JPEG/JPEG2000 coded images without having the original image as a reference is still challenging, mainly due to our limited understanding of how humans combine the various perceived artifacts to an overall quality judgment. A known approach to avoid the explicit simulation of human assessment of overall quality is the use of a neural network. Neural network approaches usually start by selecting active features from a set of generic image characteristics, a process that is, to some extent, rather ad hoc and computationally extensive. This paper shows that the complexity of the feature selection procedure can be considerably reduced by using dedicated features that describe a given artifact. The adaptive neural network is then used to learn the highly nonlinear relationship between the features describing an artifact and the overall quality rating. Experimental results show that the simplified feature selection procedure, in combination with the neural network, indeed are able to accurately predict perceived image quality of JPEG/JPEG2000 coded images.
Several attempts to integrate visual saliency information in quality metrics are described in literature, albeit with
contradictory results. The way saliency is integrated in quality metrics should reflect the mechanisms underlying the
interaction between image quality assessment and visual attention. This interaction is actually two-fold: (1) image
distortions can attract attention away from the Natural Scene Saliency (NSS), and (2) the quality assessment task in itself
can affect the way people look at an image. A subjective study was performed to analyze the deviation in attention from
NSS as a consequence of being asked to assess the quality of distorted images, and, in particular, whether, and if so how,
this deviation depended on the distortion kind and/or amount. Saliency maps were derived from eye-tracking data
obtained during scoring distorted images, and they were compared to the corresponding NSS, derived from eye-tracking
data obtained during freely looking at high quality images. The study revealed some structural differences between the
NSS maps and the ones obtained during quality assessment of the distorted images. These differences were related to the
quality level of the images; the lower the quality, the higher the deviation from the NSS was. The main change was
identified as a shrinking of the region of interest, being most evident at low quality. No evident role for the kind of
distortion in the change in saliency was found. Especially at low quality, the quality assessment task seemed to prevail
on the natural attention, forcing it to deviate in order to better evaluate the impact of artifacts.
Developing an objective metric, which automatically quantifies perceived image quality degradation induced by blur, is
highly beneficial for current digital imaging systems. In many applications, these objective metrics need to be of the noreference
(NR) type, which implies that quality prediction is based on the distorted image only. Recent progress in the
development of a NR blur metric is evident from many promising methods reported in the literature. However, there is
still room for improvement in the design of a NR metric that reliably predicts the extent to which humans perceive blur.
In this paper, we address some important issues relevant to the design as well as the application of a NR blur metric. Its
purpose is not to describe a particular metric, but rather to explain current concerns and difficulties in this field, and to
outline how these issues may be accounted for in the design of future metrics.
This paper presents a novel system that employs an adaptive neural network for the no-reference assessment of perceived
quality of JPEG/JPEG2000 coded images. The adaptive neural network simulates the human visual system as a black
box, avoiding its explicit modeling. It uses image features and the corresponding subjective quality score to learn the
unknown relationship between an image and its perceived quality. Related approaches in literature extract a considerable
number of features to form the input to the neural network. This potentially increases the system's complexity, and
consequently, may affect its prediction accuracy. Our proposed method optimizes the feature-extraction stage by
selecting the most relevant features. It shows that one can largely reduce the number of features needed for the neural
network when using gradient-based information. Additionally, the proposed method demonstrates that a common
adaptive framework can be used to support the quality estimation for both compression methods. The performance of the
method is evaluated with a publicly available database of images and their quality score. The results show that our
proposed no-reference method for the quality prediction of JPEG and JPEG2000 coded images has a comparable
performance to the leading metrics available in literature, but at a considerably lower complexity.
Manufacturers of commercial display devices continuously try to improve the perceived image quality of their products.
By applying some post processing techniques on the incoming image signal, they aim to enhance the quality level
perceived by the viewer. Applying such techniques may cause side effects on different portions of the processed image.
In order to apply these techniques effectively to improve the overall quality, it is vital to understand how important
quality is for different parts of the image. To study this effect, a three-phase experiment was conducted where observers
were asked to score images which had different levels of quality in their saliency regions than in the background areas.
The results show that the saliency area has a greater effect on the overall quality of the image than the background. This
effect increases with the increasing quality difference between the two regions. It is, therefore, important to take this
effect into consideration when trying to enhance the appearance of specific image regions.
The Single Stimulus (SS) method is often chosen to collect subjective data testing no-reference objective metrics, as it is
straightforward to implement and well standardized. At the same time, it exhibits some drawbacks; spread between
different assessors is relatively large, and the measured ratings depend on the quality range spanned by the test samples,
hence the results from different experiments cannot easily be merged . The Quality Ruler (QR) method has been
proposed to overcome these inconveniences. This paper compares the performance of the SS and QR method for
pictures impaired by Gaussian blur. The research goal is, on one hand, to analyze the advantages and disadvantages of
both methods for quality assessment and, on the other, to make quality data of blur impaired images publicly available.
The obtained results show that the confidence intervals of the QR scores are narrower than those of the SS scores. This
indicates that the QR method enhances consistency across assessors. Moreover, QR scores exhibit a higher linear
correlation with the distortion applied. In summary, for the purpose of building datasets of subjective quality, the QR
approach seems promising from the viewpoint of both consistency and repeatability.
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