The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
현재 이미지 품질 평가(IQA) 방법에서는 평가를 위해 원본 이미지가 필요합니다. 그러나 최근에는 머신러닝을 활용한 IQA 방법이 제안되었습니다. 이러한 방법은 왜곡된 이미지와 이미지 품질 간의 관계를 자동으로 학습합니다. 본 논문에서는 참조 이미지가 필요하지 않은 딥러닝 기반의 IQA 방법을 제안합니다. 우리는 왜곡 예측과 고정 필터를 갖춘 컨벌루션 신경망이 IQA 정확도를 향상시킨다는 것을 보여줍니다.
Motohiro TAKAGI
Keio University
Akito SAKURAI
Yokohama National University
Masafumi HAGIWARA
Keio University
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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},}
부
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 -