Abstract
In this paper we present a new method for classification of image degradation type based on Riesz transform coefficients and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) that employs spatial coefficients. In our method we use additional statistical parameters that give statistically better results for blur and all tested degradations together in comparison with previous methods. A new method to determine level of blur and Gaussian noise degradation in images using statistical model of natural scene is presented. We defined parameters for evaluation of level of Gaussian noise and blur degradation in images. In real world applications reference image is usually not available therefore the proposed method enables classification of image degradation by type and estimation of Gaussian noise and blur levels for any degraded image.
Highlights
In recent years, multimedia has attracted more and more attention as a research field
The new method achieves statistically significantly better results for images degraded with blur
The purpose of this paper is to find a suitable way of classification of image degradation type and to evaluate the level of Gaussian noise and blur image degradations
Summary
Multimedia has attracted more and more attention as a research field. With rapid development of different multimedia content, new methods are required for efficient evaluation of its quality (Quality of Experience, QoE). Multimedia quality can be evaluated using different methods. Subjective quality assessment is known to be the most accurate reflection of user experience (QoE). Subjective quality is often expressed as Mean Opinion Score (MOS) that represents a quality grade attributed by a standard average observer to a given video sequence. MOS grades are collected following well defined methods and procedures that have been proposed in the last decades, such as ITU-R BT.500-13 [3], whose goal is to ensure that the same experimental settings and conditions are used during different assessment campaigns
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