Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Deep Contrast Enhancement with Power Constraint for OLED
  Displays   Various power-constrained contrast enhancement (PCCE) techniques have been
applied to an organic light emitting diode (OLED) display for reducing the
power demands of the display while preserving the image quality. In this paper,
we propose a new deep learning-based PCCE scheme that constrains the power
consumption of the OLED displays while enhancing the contrast of the displayed
image. In the proposed method, the power consumption is constrained by simply
reducing the brightness a certain ratio, whereas the perceived visual quality
is preserved as much as possible by enhancing the contrast of the image using a
convolutional neural network (CNN). Furthermore, our CNN can learn the PCCE
technique without a reference image by unsupervised learning. Experimental
results show that the proposed method is superior to conventional ones in terms
of image quality assessment metrics such as a visual saliency-induced index
(VSI) and a measure of enhancement (EME).

5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con