IEICE TRANSACTIONS on Fundamentals
A Fully-Blind and Fast Image Quality Predictor with Convolutional Neural Networks
Summary :
Image quality assessment (IQA) is an inherent problem in the field of image processing. Recently, deep learning-based image quality assessment has attracted increased attention, owing to its high prediction accuracy. In this paper, we propose a fully-blind and fast image quality predictor (FFIQP) using convolutional neural networks including two strategies. First, we propose a distortion clustering strategy based on the distribution function of intermediate-layer results in the convolutional neural network (CNN) to make IQA fully blind. Second, by analyzing the relationship between image saliency information and CNN prediction error, we utilize a pre-saliency map to skip the non-salient patches for IQA acceleration. Experimental results verify that our method can achieve the high accuracy (0.978) with subjective quality scores, outperforming existing IQA methods. Moreover, the proposed method is highly computationally appealing, achieving flexible complexity performance by assigning different thresholds in the saliency map.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E101-A No.9 pp.1557-1566
- Publication Date
- 2018/09/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.E101.A.1557
- Type of Manuscript
- PAPER
- Category
- Image
Authors
Zhengxue CHENG
Waseda University
Masaru TAKEUCHI
Waseda University
Kenji KANAI
Waseda University
Jiro KATTO
Waseda University
Keyword
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Zhengxue CHENG, Masaru TAKEUCHI, Kenji KANAI, Jiro KATTO, "A Fully-Blind and Fast Image Quality Predictor with Convolutional Neural Networks" in IEICE TRANSACTIONS on Fundamentals,
vol. E101-A, no. 9, pp. 1557-1566, September 2018, doi: 10.1587/transfun.E101.A.1557.
Abstract: Image quality assessment (IQA) is an inherent problem in the field of image processing. Recently, deep learning-based image quality assessment has attracted increased attention, owing to its high prediction accuracy. In this paper, we propose a fully-blind and fast image quality predictor (FFIQP) using convolutional neural networks including two strategies. First, we propose a distortion clustering strategy based on the distribution function of intermediate-layer results in the convolutional neural network (CNN) to make IQA fully blind. Second, by analyzing the relationship between image saliency information and CNN prediction error, we utilize a pre-saliency map to skip the non-salient patches for IQA acceleration. Experimental results verify that our method can achieve the high accuracy (0.978) with subjective quality scores, outperforming existing IQA methods. Moreover, the proposed method is highly computationally appealing, achieving flexible complexity performance by assigning different thresholds in the saliency map.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E101.A.1557/_p
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@ARTICLE{e101-a_9_1557,
author={Zhengxue CHENG, Masaru TAKEUCHI, Kenji KANAI, Jiro KATTO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={A Fully-Blind and Fast Image Quality Predictor with Convolutional Neural Networks},
year={2018},
volume={E101-A},
number={9},
pages={1557-1566},
abstract={Image quality assessment (IQA) is an inherent problem in the field of image processing. Recently, deep learning-based image quality assessment has attracted increased attention, owing to its high prediction accuracy. In this paper, we propose a fully-blind and fast image quality predictor (FFIQP) using convolutional neural networks including two strategies. First, we propose a distortion clustering strategy based on the distribution function of intermediate-layer results in the convolutional neural network (CNN) to make IQA fully blind. Second, by analyzing the relationship between image saliency information and CNN prediction error, we utilize a pre-saliency map to skip the non-salient patches for IQA acceleration. Experimental results verify that our method can achieve the high accuracy (0.978) with subjective quality scores, outperforming existing IQA methods. Moreover, the proposed method is highly computationally appealing, achieving flexible complexity performance by assigning different thresholds in the saliency map.},
keywords={},
doi={10.1587/transfun.E101.A.1557},
ISSN={1745-1337},
month={September},}
Copy
TY - JOUR
TI - A Fully-Blind and Fast Image Quality Predictor with Convolutional Neural Networks
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1557
EP - 1566
AU - Zhengxue CHENG
AU - Masaru TAKEUCHI
AU - Kenji KANAI
AU - Jiro KATTO
PY - 2018
DO - 10.1587/transfun.E101.A.1557
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E101-A
IS - 9
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - September 2018
AB - Image quality assessment (IQA) is an inherent problem in the field of image processing. Recently, deep learning-based image quality assessment has attracted increased attention, owing to its high prediction accuracy. In this paper, we propose a fully-blind and fast image quality predictor (FFIQP) using convolutional neural networks including two strategies. First, we propose a distortion clustering strategy based on the distribution function of intermediate-layer results in the convolutional neural network (CNN) to make IQA fully blind. Second, by analyzing the relationship between image saliency information and CNN prediction error, we utilize a pre-saliency map to skip the non-salient patches for IQA acceleration. Experimental results verify that our method can achieve the high accuracy (0.978) with subjective quality scores, outperforming existing IQA methods. Moreover, the proposed method is highly computationally appealing, achieving flexible complexity performance by assigning different thresholds in the saliency map.
ER -
English
