No-reference image quality assessment based on BNB measurement

Ruigang Fang; Dapeng Wu

doi:10.1109/ChinaSIP.2013.6625396

No-reference image quality assessment based on BNB measurement

Ruigang Fang
, Dapeng Wu

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

6 Citations (Scopus)

Abstract

In this paper, we present a no-reference image quality assessment method, which we call BNB (an acronym for Blurriness, Noisiness, Blockiness). Our BNB method quantifies blurriness, noisiness and blockiness of a given image, which are considered three critical factors that affect users' quality of experience (QoE). The well designed BNB metrics are based on the observation that the difference between any two adjacent pixel values follows a Laplace distribution with mean zero, and the Laplace distribution will change differently under different artifacts, i.e., blurriness, noisiness and blockiness. Then we use supervised learning to map the three BNB metrics of an image to a human perception score. Experimental results show that the image quality score obtained by our BNB method has higher correlation with human perceptual score and our method needs much less computation, compared to existing no-reference image quality assessment methods. © 2013 IEEE.

Original language	English
Title of host publication	2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings
Pages	528-532
DOIs	https://doi.org/10.1109/ChinaSIP.2013.6625396
Publication status	Published - 2013
Externally published	Yes
Event	2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Beijing, China Duration: 6 Jul 2013 → 10 Jul 2013 https://ieeexplore.ieee.org/xpl/conhome/6611294/proceeding

Publication series

Name	2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings

Conference

Conference	2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013
Place	China
City	Beijing
Period	6/07/13 → 10/07/13
Internet address	https://ieeexplore.ieee.org/xpl/conhome/6611294/proceeding

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

Research Keywords

artifact metric
BNB
IQA
Laplace distribution
No-reference

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10.1109/ChinaSIP.2013.6625396

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Cite this

Fang, R., & Wu, D. (2013). No-reference image quality assessment based on BNB measurement. In 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings (pp. 528-532). Article 6625396 (2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings). https://doi.org/10.1109/ChinaSIP.2013.6625396

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title = "No-reference image quality assessment based on BNB measurement",

abstract = "In this paper, we present a no-reference image quality assessment method, which we call BNB (an acronym for Blurriness, Noisiness, Blockiness). Our BNB method quantifies blurriness, noisiness and blockiness of a given image, which are considered three critical factors that affect users' quality of experience (QoE). The well designed BNB metrics are based on the observation that the difference between any two adjacent pixel values follows a Laplace distribution with mean zero, and the Laplace distribution will change differently under different artifacts, i.e., blurriness, noisiness and blockiness. Then we use supervised learning to map the three BNB metrics of an image to a human perception score. Experimental results show that the image quality score obtained by our BNB method has higher correlation with human perceptual score and our method needs much less computation, compared to existing no-reference image quality assessment methods. {\textcopyright} 2013 IEEE.",

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Fang, R & Wu, D 2013, No-reference image quality assessment based on BNB measurement. in 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings., 6625396, 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings, pp. 528-532, 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013, Beijing, China, 6/07/13. https://doi.org/10.1109/ChinaSIP.2013.6625396

No-reference image quality assessment based on BNB measurement. / Fang, Ruigang; Wu, Dapeng.
2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings. 2013. p. 528-532 6625396 (2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings).

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

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AU - Wu, Dapeng

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2013

Y1 - 2013

N2 - In this paper, we present a no-reference image quality assessment method, which we call BNB (an acronym for Blurriness, Noisiness, Blockiness). Our BNB method quantifies blurriness, noisiness and blockiness of a given image, which are considered three critical factors that affect users' quality of experience (QoE). The well designed BNB metrics are based on the observation that the difference between any two adjacent pixel values follows a Laplace distribution with mean zero, and the Laplace distribution will change differently under different artifacts, i.e., blurriness, noisiness and blockiness. Then we use supervised learning to map the three BNB metrics of an image to a human perception score. Experimental results show that the image quality score obtained by our BNB method has higher correlation with human perceptual score and our method needs much less computation, compared to existing no-reference image quality assessment methods. © 2013 IEEE.

AB - In this paper, we present a no-reference image quality assessment method, which we call BNB (an acronym for Blurriness, Noisiness, Blockiness). Our BNB method quantifies blurriness, noisiness and blockiness of a given image, which are considered three critical factors that affect users' quality of experience (QoE). The well designed BNB metrics are based on the observation that the difference between any two adjacent pixel values follows a Laplace distribution with mean zero, and the Laplace distribution will change differently under different artifacts, i.e., blurriness, noisiness and blockiness. Then we use supervised learning to map the three BNB metrics of an image to a human perception score. Experimental results show that the image quality score obtained by our BNB method has higher correlation with human perceptual score and our method needs much less computation, compared to existing no-reference image quality assessment methods. © 2013 IEEE.

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KW - BNB

KW - IQA

KW - Laplace distribution

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BT - 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings

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ER -

Fang R, Wu D. No-reference image quality assessment based on BNB measurement. In 2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings. 2013. p. 528-532. 6625396. (2013 IEEE China Summit and International Conference on Signal and Information Processing, ChinaSIP 2013 - Proceedings). doi: 10.1109/ChinaSIP.2013.6625396

No-reference image quality assessment based on BNB measurement

Abstract

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Bibliographical note

Research Keywords

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