Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Confusing Image Quality Assessment: Towards Better Augmented Reality
  Experience   With the development of multimedia technology, Augmented Reality (AR) has
become a promising next-generation mobile platform. The primary value of AR is
to promote the fusion of digital contents and real-world environments, however,
studies on how this fusion will influence the Quality of Experience (QoE) of
these two components are lacking. To achieve better QoE of AR, whose two layers
are influenced by each other, it is important to evaluate its perceptual
quality first. In this paper, we consider AR technology as the superimposition
of virtual scenes and real scenes, and introduce visual confusion as its basic
theory. A more general problem is first proposed, which is evaluating the
perceptual quality of superimposed images, i.e., confusing image quality
assessment. A ConFusing Image Quality Assessment (CFIQA) database is
established, which includes 600 reference images and 300 distorted images
generated by mixing reference images in pairs. Then a subjective quality
perception study and an objective model evaluation experiment are conducted
towards attaining a better understanding of how humans perceive the confusing
images. An objective metric termed CFIQA is also proposed to better evaluate
the confusing image quality. Moreover, an extended ARIQA study is further
conducted based on the CFIQA study. We establish an ARIQA database to better
simulate the real AR application scenarios, which contains 20 AR reference
images, 20 background (BG) reference images, and 560 distorted images generated
from AR and BG references, as well as the correspondingly collected subjective
quality ratings. We also design three types of full-reference (FR) IQA metrics
to study whether we should consider the visual confusion when designing
corresponding IQA algorithms. An ARIQA metric is finally proposed for better
evaluating the perceptual quality of AR images.

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The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
26
The categories of this paper are: bayes-an
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org