Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: UNIQUE: Unsupervised Image Quality Estimation   In this paper, we estimate perceived image quality using sparse
representations obtained from generic image databases through an unsupervised
learning approach. A color space transformation, a mean subtraction, and a
whitening operation are used to enhance descriptiveness of images by reducing
spatial redundancy; a linear decoder is used to obtain sparse representations;
and a thresholding stage is used to formulate suppression mechanisms in a
visual system. A linear decoder is trained with 7 GB worth of data, which
corresponds to 100,000 8x8 image patches randomly obtained from nearly 1,000
images in the ImageNet 2013 database. A patch-wise training approach is
preferred to maintain local information. The proposed quality estimator UNIQUE
is tested on the LIVE, the Multiply Distorted LIVE, and the TID 2013 databases
and compared with thirteen quality estimators. Experimental results show that
UNIQUE is generally a top performing quality estimator in terms of accuracy,
consistency, linearity, and monotonic behavior.

21
The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
11
The categories of this paper are: hep-lat
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas