A Two-Step Framework for Constructing Blind Image Quality Indices
Abstract
Present day no-reference/no-reference image quality assessment (NR IQA) algorithms usually assume that the distortion affecting the image is known. This is a limiting assumption for practical applications, since in a majority of cases the distortions in the image are unknown. We propose a new two-step framework for no-reference image quality assessment based on natural scene statistics (NSS). Once trained, the framework does not require any knowledge of the distorting process and the framework is modular in that it can be extended to any number of distortions. We describe the framework for blind image quality assessment and a version of this framework-the blind image quality index (BIQI) is evaluated on the LIVE image quality assessment database. A software release of BIQI has been made available online: http://live.ece.utexas.edu/research/quality/BIQI_release.zip.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Training-based no reference QA techniques perform at least as well as mean squared error.
“Training based NR QA techniques have resulted in algorithms that perform at least as well as mean squared error, which has the benefit of a reference image [9], [10].”
From RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment (2012) · cites this paper as [9]
Cites
The Nature of Statistical Learning Theory
Vapnik, V. (2000) · Statistics for Engineering and Information Science · doi:10.1007/978-1-4757-3264-1 · Cited by 46,387 · see citing works
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Daubechies, I. (1992) · doi:10.1137/1.9781611970104 · Cited by 15,979
Daubechies, I. C., & Heil, C. (1992) · Computers in Physics · doi:10.1063/1.4823127 · Cited by 15,729 · see citing works
A Statistical Evaluation of Recent Full Reference Image Quality Assessment Algorithms
Sheikh, H. R., Sabir, M. F., & Bovik, A. C. (2006) · IEEE Transactions on Image Processing · doi:10.1109/tip.2006.881959 · Cited by 3,018 · see citing works
Self-citation(1999) · doi:10.1162/089976600300015565 · Cited by 2,799 · see citing works
The Nature of Statistical Learning
Vapnik, V. N. (1995) · Medical Entomology and Zoology · Cited by 2,295 · see citing works
No-reference perceptual quality assessment of JPEG compressed images
Wang, Z., Sheikh, H. R., & Bovik, A. C. (2003) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2002.1038064 · Cited by 839 · see citing works
Self-citationNo-reference quality assessment using natural scene statistics: JPEG2000
Sheikh, H. R., Bovik, A. C., & Cormack, L. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.854492 · Cited by 575 · see citing works
Self-citationSharifi, K., & Leon-Garcia, A. (1995) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/76.350779 · Cited by 540 · see citing works
Perceptual blur and ringing metrics: application to JPEG2000
Marziliano, P., Frederic Dufaux, A., WINKLER, S. S., & Ebrahimi, T. (2003) · Signal Processing Image Communication · doi:10.1016/j.image.2003.08.003 · Cited by 491 · see citing works
On Advances in Statistical Modeling of Natural Images
Srivastava, A., Lee, A. B., Simoncelli, E. P., & Zhu, S. (2003) · Journal of Mathematical Imaging and Vision · doi:10.1023/a:1021889010444 · Cited by 483 · see citing works
A new sharpness metric based on local kurtosis, edge and energy information
Caviedes, J. E., & Oberti, F. (2003) · Signal Processing Image Communication · doi:10.1016/j.image.2003.08.002 · Cited by 135 · see citing works
No-reference video quality metric based on artifact measurements
Farias, M. C. Q., & Mitra, S. K. (2005) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2005.1530348 · Cited by 107 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
No-Reference Image Quality Assessment in the Spatial Domain
Mittal, A., Moorthy, A. K., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2012.2214050 · Cited by 6,064 · see citing works
Self-citationBlind Image Quality Assessment: From Natural Scene Statistics to Perceptual Quality
Moorthy, A. K., & Bovik, A. C. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2147325 · Cited by 1,763 · see citing works
Self-citationA Feature-Enriched Completely Blind Image Quality Evaluator
Zhang, L., Zhang, L., & Bovik, A. C. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2426416 · Cited by 1,311 · see citing works
Self-citationWaterloo Exploration Database: New Challenges for Image Quality Assessment Models
Ma, K., Duanmu, Z., Wu, Q., Wang, Z., Yong, H., Li, H., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2631888 · Cited by 789 · see citing works
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment
Hosu, V., Lin, H., Sziranyi, T., & Saupe, D. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.2967829 · Cited by 759 · see citing works
Blind Image Quality Assessment Using Joint Statistics of Gradient Magnitude and Laplacian Features
Xue, W., Mou, X., Zhang, L., Bovik, A. C., & Feng, X. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2355716 · Cited by 641 · see citing works
Self-citationKADID-10k: A Large-scale Artificially Distorted IQA Database
Lin, H., Hosu, V., & Saupe, D. (2019) · 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex.2019.8743252 · Cited by 523 · see citing works
Blind Prediction of Natural Video Quality
Saad, M. A., Bovik, A. C., & Charrier, C. (2014) · IEEE Transactions on Image Processing · doi:10.1109/tip.2014.2299154 · Cited by 471 · see citing works
Self-citationNo-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency
Golestaneh, S. A., Dadsetan, S., & Kitani, K. M. (2022) · 2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv51458.2022.00404 · Cited by 465 · see citing works
RRED Indices: Reduced Reference Entropic Differencing for Image Quality Assessment
Soundararajan, R., & Bovik, A. C. (2012) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2166082 · Cited by 337 · see citing works
Self-citationUGC-VQA: Benchmarking Blind Video Quality Assessment for User Generated Content
Tu, Z., Wang, Y., Birkbeck, N., Adsumilli, B., & Bovik, A. C. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3072221 · Cited by 363 · see citing works
“Successful blind picture quality assessment (BIQA) models of this type have been developed in the wavelet (BIQI [48], DIIVINE [37], C-DIIVINE [49]), discrete cosine transform (BLIINDS [50], BLIINDS-II [51]), curvelet [52], and spatial intensity domains (NIQE [53], BRISQUE [38]), and have further been extended to video…”
MethodSelf-citationReduced- and No-Reference Image Quality Assessment
Wang, Z., & Bovik, A. (2011) · IEEE Signal Processing Magazine · doi:10.1109/msp.2011.942471 · Cited by 280 · see citing works
Self-citationDiff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model
Yi, X., Xu, H., Zhang, H., Tang, L., & Ma, J. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.01130 · Cited by 305 · see citing works
QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey
Barakabitze, A. A., Barman, N., Ahmad, A., Zadtootaghaj, S., Sun, L., Martini, M. G., & Atzori, L. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2958784 · Cited by 239 · see citing works
Barman, N., Jammeh, E., Ghorashi, S. A., & Martini, M. G. (2019) · IEEE Access · doi:10.1109/access.2019.2920477 · Cited by 71 · see citing works
Quality assessment in the era of large models: A survey
Zhang, Z., Zhou, Y., Li, C., Zhao, B., Liu, X., & Zhai, G. (2025) · ACM Transactions on Multimedia Computing, Communications, and Applications · doi:10.1145/3722559 · Cited by 23 · see citing works
No-reference image quality assessment based on global awareness
Hu, Z., Yang, G., Du, Z., Huang, X., Zhang, P., & Liu, D. (2024) · PLOS ONE · doi:10.1371/journal.pone.0310206 · Cited by 5 · see citing works
- Authors
- Moorthy, A. K., & Bovik, A. C.
- Affiliations
- The University of Texas at Austin (US)
- ORCID
- Bovik, A.C.
- Venue
- IEEE Signal Processing Letters
- Published
- 2010-05
- Volume
- 17
- Issue
- 5
- Pages
- 513-516
- ISSN
- 1070-9908 (ISSN-L)
- 1558-2361
- OpenAlex
- W2163370434
- Cited by
- 1,196
- References
- 14
- Citations by year
- 2026: 32 · 2025: 45 · 2024: 74 · 2023: 76 · 2022: 83 · 2021: 92 · 2020: 72 · 2019: 109 · 2018: 113 · 2017: 102 · 2016: 102 · 2015: 86 · 2014: 74 · 2013: 55 · 2012: 41
- FWCI
- 50.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Signal Processing Letters (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- no-reference image quality assessment, natural scene statistics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
