Deep blind image quality assessment by employing FR-IQA

Jongyoo Kim; Sanghoon Lee

doi:10.1109/ICIP.2017.8296869

Deep blind image quality assessment by employing FR-IQA

Jongyoo Kim, Sanghoon Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Citations (Scopus)

Abstract

In this paper, we propose a convolutional neural network (CNN)-based no-reference image quality assessment (NR-IQA). Though deep learning has yielded superior performance in a number of computer vision studies, applying the deep CNN to the NR-IQA framework is not straightforward, since we face a few critical problems: 1) lack of training data; 2) absence of local ground truth targets. To alleviate these problems, we employ the full-reference image quality assessment (FR-IQA) metrics as intermediate training targets of the CNN. In addition, we incorporate the pooling stage in the training stage, so that the whole parameters of the model can be optimized in an end-to-end framework. The proposed model, named as a blind image evaluator based on a convolutional neural network (BIECON), achieves state-of-the-art prediction accuracy that is comparable with that of FR-IQA methods.

Original language	English
Title of host publication	2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
Publisher	IEEE Computer Society
Pages	3180-3184
Number of pages	5
ISBN (Electronic)	9781509021758
DOIs	https://doi.org/10.1109/ICIP.2017.8296869
Publication status	Published - 2018 Feb 20
Event	24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China Duration: 2017 Sept 17 → 2017 Sept 20

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2017-September
ISSN (Print)	1522-4880

Other

Other	24th IEEE International Conference on Image Processing, ICIP 2017
Country/Territory	China
City	Beijing
Period	17/9/17 → 17/9/20

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. 2016R1A2B2014525).

Publisher Copyright:
© 2017 IEEE.

All Science Journal Classification (ASJC) codes

Software
Computer Vision and Pattern Recognition
Signal Processing

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10.1109/ICIP.2017.8296869

Cite this

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abstract = "In this paper, we propose a convolutional neural network (CNN)-based no-reference image quality assessment (NR-IQA). Though deep learning has yielded superior performance in a number of computer vision studies, applying the deep CNN to the NR-IQA framework is not straightforward, since we face a few critical problems: 1) lack of training data; 2) absence of local ground truth targets. To alleviate these problems, we employ the full-reference image quality assessment (FR-IQA) metrics as intermediate training targets of the CNN. In addition, we incorporate the pooling stage in the training stage, so that the whole parameters of the model can be optimized in an end-to-end framework. The proposed model, named as a blind image evaluator based on a convolutional neural network (BIECON), achieves state-of-the-art prediction accuracy that is comparable with that of FR-IQA methods.",

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Kim, J & Lee, S 2018, Deep blind image quality assessment by employing FR-IQA. in 2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. Proceedings - International Conference on Image Processing, ICIP, vol. 2017-September, IEEE Computer Society, pp. 3180-3184, 24th IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, 17/9/17. https://doi.org/10.1109/ICIP.2017.8296869

Deep blind image quality assessment by employing FR-IQA. / Kim, Jongyoo; Lee, Sanghoon.
2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings. IEEE Computer Society, 2018. p. 3180-3184 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2017-September).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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Deep blind image quality assessment by employing FR-IQA

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