4,145 research outputs found
A Detail Based Method for Linear Full Reference Image Quality Prediction
In this paper, a novel Full Reference method is proposed for image quality
assessment, using the combination of two separate metrics to measure the
perceptually distinct impact of detail losses and of spurious details. To this
purpose, the gradient of the impaired image is locally decomposed as a
predicted version of the original gradient, plus a gradient residual. It is
assumed that the detail attenuation identifies the detail loss, whereas the
gradient residuals describe the spurious details. It turns out that the
perceptual impact of detail losses is roughly linear with the loss of the
positional Fisher information, while the perceptual impact of the spurious
details is roughly proportional to a logarithmic measure of the signal to
residual ratio. The affine combination of these two metrics forms a new index
strongly correlated with the empirical Differential Mean Opinion Score (DMOS)
for a significant class of image impairments, as verified for three independent
popular databases. The method allowed alignment and merging of DMOS data coming
from these different databases to a common DMOS scale by affine
transformations. Unexpectedly, the DMOS scale setting is possible by the
analysis of a single image affected by additive noise.Comment: 15 pages, 9 figures. Copyright notice: The paper has been accepted
for publication on the IEEE Trans. on Image Processing on 19/09/2017 and the
copyright has been transferred to the IEE
A Reduced Reference Image Quality Measure Using Bessel K Forms Model for Tetrolet Coefficients
In this paper, we introduce a Reduced Reference Image Quality Assessment
(RRIQA) measure based on the natural image statistic approach. A new adaptive
transform called "Tetrolet" is applied to both reference and distorted images.
To model the marginal distribution of tetrolet coefficients Bessel K Forms
(BKF) density is proposed. Estimating the parameters of this distribution
allows to summarize the reference image with a small amount of side
information. Five distortion measures based on the BKF parameters of the
original and processed image are used to predict quality scores. A comparison
between these measures is presented showing a good consistency with human
judgment
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment
(IQA). The network is trained end-to-end and comprises ten convolutional layers
and five pooling layers for feature extraction, and two fully connected layers
for regression, which makes it significantly deeper than related IQA models.
Unique features of the proposed architecture are that: 1) with slight
adaptations it can be used in a no-reference (NR) as well as in a
full-reference (FR) IQA setting and 2) it allows for joint learning of local
quality and local weights, i.e., relative importance of local quality to the
global quality estimate, in an unified framework. Our approach is purely
data-driven and does not rely on hand-crafted features or other types of prior
domain knowledge about the human visual system or image statistics. We evaluate
the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the
LIVE In the wild image quality challenge database and show superior performance
to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation
shows a high ability to generalize between different databases, indicating a
high robustness of the learned features
Understanding user experience of mobile video: Framework, measurement, and optimization
Since users have become the focus of product/service design in last decade, the term User eXperience (UX) has been frequently used in the field of Human-Computer-Interaction (HCI). Research on UX facilitates a better understanding of the various aspects of the user’s interaction with the product or service. Mobile video, as a new and promising service and research field, has attracted great attention. Due to the significance of UX in the success of mobile video (Jordan, 2002), many researchers have centered on this area, examining users’ expectations, motivations, requirements, and usage context. As a result, many influencing factors have been explored (Buchinger, Kriglstein, Brandt & Hlavacs, 2011; Buchinger, Kriglstein & Hlavacs, 2009). However, a general framework for specific mobile video service is lacking for structuring such a great number of factors. To measure user experience of multimedia services such as mobile video, quality of experience (QoE) has recently become a prominent concept. In contrast to the traditionally used concept quality of service (QoS), QoE not only involves objectively measuring the delivered service but also takes into account user’s needs and desires when using the service, emphasizing the user’s overall acceptability on the service. Many QoE metrics are able to estimate the user perceived quality or acceptability of mobile video, but may be not enough accurate for the overall UX prediction due to the complexity of UX. Only a few frameworks of QoE have addressed more aspects of UX for mobile multimedia applications but need be transformed into practical measures. The challenge of optimizing UX remains adaptations to the resource constrains (e.g., network conditions, mobile device capabilities, and heterogeneous usage contexts) as well as meeting complicated user requirements (e.g., usage purposes and personal preferences). In this chapter, we investigate the existing important UX frameworks, compare their similarities and discuss some important features that fit in the mobile video service. Based on the previous research, we propose a simple UX framework for mobile video application by mapping a variety of influencing factors of UX upon a typical mobile video delivery system. Each component and its factors are explored with comprehensive literature reviews. The proposed framework may benefit in user-centred design of mobile video through taking a complete consideration of UX influences and in improvement of mobile videoservice quality by adjusting the values of certain factors to produce a positive user experience. It may also facilitate relative research in the way of locating important issues to study, clarifying research scopes, and setting up proper study procedures. We then review a great deal of research on UX measurement, including QoE metrics and QoE frameworks of mobile multimedia. Finally, we discuss how to achieve an optimal quality of user experience by focusing on the issues of various aspects of UX of mobile video. In the conclusion, we suggest some open issues for future study
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