The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Blind Image Quality Assessment: A Natural Scene Statistics Approach in the DCT Domain
Abstract
We develop an efficient, general-purpose, blind/noreference image quality assessment (NR-IQA) algorithm using a natural scene statistics (NSS) model of discrete cosine transform (DCT) coefficients. The algorithm is computationally appealing, given the availability of platforms optimized for DCT computation. The approach relies on a simple Bayesian inference model to predict image quality scores given certain extracted features. The features are based on an NSS model of the image DCT coefficients. The estimated parameters of the model are utilized to form features that are indicative of perceptual quality. These features are used in a simple Bayesian inference approach to predict quality scores. The resulting algorithm, which we name BLIINDS-II, requires minimal training and adopts a simple probabilistic model for score prediction. Given the extracted features from a test image, the quality score that maximizes the probability of the empirically determined inference model is chosen as the predicted quality score of that image. When tested on the LIVE IQA database, BLIINDS-II is shown to correlate highly with human judgments of quality, at a level that is competitive with the popular SSIM index.
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Cites
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A. C., Sheikh, H. R., & Simoncelli, E. P. (2004) · IEEE Transactions on Image Processing · doi:10.1109/tip.2003.819861 · Cited by 57,892
Self-citationMultiscale structural similarity for image quality assessment
Wang, Z., Simoncelli, E. P., & Bovik, A. C. (2004) · The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003 · doi:10.1109/acssc.2003.1292216 · Cited by 6,253
Self-citationA new standardized method for objectively measuring video quality
Pinson, M. H., & Wolf, S. (2004) · IEEE Transactions on Broadcasting · doi:10.1109/tbc.2004.834028 · Cited by 1,182 · cited 1×
“This kind of “percentile pooling” has been observed to result in improved correlations with subjective perception of quality [23], [45].”
BackgroundVSNR: A Wavelet-Based Visual Signal-to-Noise Ratio for Natural Images
Chandler, D. M., & Hemami, S. S. (2007) · IEEE Transactions on Image Processing · doi:10.1109/tip.2007.901820 · Cited by 1,147
Motion Tuned Spatio-Temporal Quality Assessment of Natural Videos
Seshadrinathan, K., & Bovik, A. C. (2010) · IEEE Transactions on Image Processing · doi:10.1109/tip.2009.2034992 · Cited by 708 · cited 1×
“Unlike much of the prior work on image/video quality assessment (QA) [1], [2], [4], [23], [24], we make little direct use of specific perceptual models such as area V1 cortical Fig.”
BackgroundSelf-citationTeo, P. C., & Heeger, D. J. (2002) · Proceedings of 1st International Conference on Image Processing · doi:10.1109/icip.1994.413502 · Cited by 471
Blind measurement of blocking artifacts in images
Wang, Z., Bovik, A. C., & Evan, B. L. (2002) · Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101) · doi:10.1109/icip.2000.899622 · Cited by 413
Self-citationBlind image quality assessment through anisotropy
Gabarda, S., & Cristóbal, G. (2007) · Journal of the Optical Society of America A · doi:10.1364/josaa.24.000b42 · Cited by 301
Cited by
Making a “Completely Blind” Image Quality Analyzer
Mittal, A., Soundararajan, R., & Bovik, A. C. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2012.2227726 · Cited by 6,766
Self-citationNo-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
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
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
Massive Online Crowdsourced Study of Subjective and Objective Picture Quality
Ghadiyaram, D., & Bovik, A. C. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2500021 · Cited by 782
Self-citation
Metadata
- Authors
- Saad, M. A., Bovik, A. C., & Charrier, C.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2012-08
- Volume
- 21
- Issue
- 8
- Pages
- 3339-3352
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W2162692770
- Cited by
- 1,751
- References
- 8
- FWCI
- 76.72
- Type
- journal article
- Language
- en
- Open access
- closed
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