Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters
Summary :
Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.
- Publication
- IEICE TRANSACTIONS on Information Vol.E102-D No.11 pp.2265-2266
- Publication Date
- 2019/11/01
- Publicized
- 2019/08/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2018EDL8272
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Motohiro TAKAGI
Keio University
Akito SAKURAI
Yokohama National University
Masafumi HAGIWARA
Keio University
Keyword
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Motohiro TAKAGI, Akito SAKURAI, Masafumi HAGIWARA, "Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters" in IEICE TRANSACTIONS on Information,
vol. E102-D, no. 11, pp. 2265-2266, November 2019, doi: 10.1587/transinf.2018EDL8272.
Abstract: Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8272/_p
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@ARTICLE{e102-d_11_2265,
author={Motohiro TAKAGI, Akito SAKURAI, Masafumi HAGIWARA, },
journal={IEICE TRANSACTIONS on Information},
title={Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters},
year={2019},
volume={E102-D},
number={11},
pages={2265-2266},
abstract={Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.},
keywords={},
doi={10.1587/transinf.2018EDL8272},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Discriminative Convolutional Neural Network for Image Quality Assessment with Fixed Convolution Filters
T2 - IEICE TRANSACTIONS on Information
SP - 2265
EP - 2266
AU - Motohiro TAKAGI
AU - Akito SAKURAI
AU - Masafumi HAGIWARA
PY - 2019
DO - 10.1587/transinf.2018EDL8272
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E102-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2019
AB - Current image quality assessment (IQA) methods require the original images for evaluation. However, recently, IQA methods that use machine learning have been proposed. These methods learn the relationship between the distorted image and the image quality automatically. In this paper, we propose an IQA method based on deep learning that does not require a reference image. We show that a convolutional neural network with distortion prediction and fixed filters improves the IQA accuracy.
ER -
English
