A Reduced Reference Image Quality Measure Using Bessel K Forms Model for Tetrolet Coefficients

In this paper, we introduce a Reduced Reference Image Quality Assessment (RRIQA) measure based on the natural image statistic approach. A new adaptive transform called "Tetrolet" is applied to both reference and distorted images. To model the marginal distribution of tetrolet coefficients Bessel K Forms (BKF) density is proposed. Estimating the parameters of this distribution allows to summarize the reference image with a small amount of side information. Five distortion measures based on the BKF parameters of the original and processed image are used to predict quality scores. A comparison between these measures is presented showing a good consistency with human judgment

Image Quality Estimation: Soft-ware for Objective Evaluation

Digital images are widely used in our daily lives and the quality of images is important to the viewing experience. Low quality images may be blurry or contain noise or compression artifacts. Humans can easily estimate image quality, but it is not practical to use human subjects to measure image quality in real applications. Image Quality Estimators (QE) are algorithms that evaluate image qualities automatically. These QEs compute scores of any input images to represent their qualities. This thesis mainly focuses on evaluating the performance of QEs. Two approaches used in this work are objective software analysis and the subjective database design. For the first, we create a software consisting of functional modules to test QE performances. These modules can load images from subjective databases or generate distortion images from any input images. Their QE scores are computed and analyzed by the statistical method module so that they can be easily interpreted and reported. Some modules in this software are combined and formed into a published software package: Stress Testing Image Quality Estimators (STIQE). In addition to the QE analysis software, a new subjective database is designed and implemented using both online and in-lab subjective tests. The database is designed using the pairwise comparison method and the subjective quality scores are computed using the Bradley-Terry model and Maximum Likelihood Estimation (MLE). While four testing phases are designed for this databases, only phase 1 is reported in this work