Journal
IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume 21, Issue 8, Pages 3339-3352Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2012.2191563
Keywords
Discrete cosine transform (DCT); generalized Gaussian density; natural scene statistics; no-reference image quality assessment
Funding
- Intel
- Cisco, Inc., under the VAWN
- [CSOSG ANR-08-SECU-007-04]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1116656] Funding Source: National Science Foundation
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We develop an efficient general-purpose blind/no-reference image quality assessment (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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