We propose an efficient blind/no-reference image quality assessment algorithm using a log-derivative statistical model of natural scenes. Our method, called DErivative Statistics-based QUality Evaluator (DESIQUE), extracts image quality-related statistical features at two image scales in both the spatial and frequency domains. In the spatial domain, normalized pixel values of an image are modeled in two ways: pointwise-based statistics for single pixel values and pairwise-based log-derivative statistics for the relationship of pixel pairs. In the frequency domain, log-Gabor filters are used to extract the fine scales of the image, which are also modeled by the log-derivative statistics. All of these statistics can be fitted by a generalized Gaussian distribution model, and the estimated parameters are fed into combined frameworks to estimate image quality. We train our models on the LIVE database by using optimized support vector machine learning. Experiment results tested on other databases show that the proposed algorithm not only yields a substantial improvement in predictive performance as compared to other state-of-the-art no-reference image quality assessment methods, but also maintains a high computational efficiency.
In this paper, we propose a new method for blind/no-reference image quality assessment based on the log-
derivative statistics of natural scenes. The new method, called DErivative Statistics-based Image QUality Eval-
uator (DESIQUE), extracts image quality-related statistical features at two image scales in both the spatial and
frequency domains, upon which a two-stage framework is employed to evaluate image quality. In the spatial
domain, normalized luminance values of an image are modeled in two ways: point-wise based statistics for sin-
gle pixel values and pairwise-based log-derivative statistics for the relationship of pixel pairs. In the frequency
domain, log-Gabor filters are used to extract the high frequency component of an image, which is also modeled
by the log-derivative statistics. All of these statistics are characterized by a generalized Gaussian distribution
model, the parameters of which form the underlying features of the proposed method. Experiment results show
that DESIQUE not only leads to considerable performance improvements, but also maintains high computational
efficiency.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.