Discriminative convolutional neural network for image quality assessment with fixed convolution filters

Motohiro Takagi; Akito Sakurai; Masafumi Hagiwara

doi:10.1587/transinf.2018EDL8272

Discriminative convolutional neural network for image quality assessment with fixed convolution filters

Motohiro Takagi, Akito Sakurai, Masafumi Hagiwara

Department of Information and Computer Science

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	2265-2266
Number of pages	2
Journal	IEICE Transactions on Information and Systems
Volume	E102D
Issue number	11
DOIs	https://doi.org/10.1587/transinf.2018EDL8272
Publication status	Published - 2019

Keywords

Convolutional neural network
Image quality assessment
Subjective image quality

ASJC Scopus subject areas

Software
Hardware and Architecture
Computer Vision and Pattern Recognition
Electrical and Electronic Engineering
Artificial Intelligence

Access to Document

10.1587/transinf.2018EDL8272

Cite this

@article{a1f02cf3412d449982f4601b61f28e9e,

title = "Discriminative convolutional neural network for image quality assessment with fixed convolution filters",

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 = "Convolutional neural network, Image quality assessment, Subjective image quality",

author = "Motohiro Takagi and Akito Sakurai and Masafumi Hagiwara",

year = "2019",

doi = "10.1587/transinf.2018EDL8272",

language = "English",

volume = "E102D",

pages = "2265--2266",

journal = "IEICE Transactions on Information and Systems",

issn = "0916-8532",

publisher = "Maruzen Co., Ltd/Maruzen Kabushikikaisha",

number = "11",

}

TY - JOUR

T1 - Discriminative convolutional neural network for image quality assessment with fixed convolution filters

AU - Takagi, Motohiro

AU - Sakurai, Akito

AU - Hagiwara, Masafumi

PY - 2019

Y1 - 2019

N2 - 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.

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.

KW - Convolutional neural network

KW - Image quality assessment

KW - Subjective image quality

UR - http://www.scopus.com/inward/record.url?scp=85075425689&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075425689&partnerID=8YFLogxK

U2 - 10.1587/transinf.2018EDL8272

DO - 10.1587/transinf.2018EDL8272

M3 - Article

AN - SCOPUS:85075425689

SN - 0916-8532

VL - E102D

SP - 2265

EP - 2266

JO - IEICE Transactions on Information and Systems

JF - IEICE Transactions on Information and Systems

IS - 11

ER -

Discriminative convolutional neural network for image quality assessment with fixed convolution filters

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this