Abstract
Blind image quality assessment (BIQA) aims to use objective measures for predicting the quality score of distorted images without any prior information regarding the reference image. Several BIQA techniques are proposed in literature that use a two-step approach, i.e., feature extraction for distortion classification and regression for predicting the quality score. In this paper, a three-step approach is proposed that aims to improve the performance of BIQA techniques. In the first step, feature extraction is performed using existing BIQA techniques to determine the distortion type. Secondly, features are selected for each distortion type based on the mean value of Spearman rank ordered correlation constant (SROCC) and linear correlation constant (LCC). Lastly, distortion-specific features are used by regression model to predict the quality score. Experimental results show that the predicted quality score using distortion-specific features strongly correlates with the subjective quality score, improves the overall performance of existing BIQA techniques, and reduces the processing time.
Highlights
In recent years, multimedia content has become a significant part of our lives
4 Conclusion Blind image quality assessment (BIQA) techniques proposed in literature use the same set of features for all the distortion types to evaluate the quality score of images
Each distortion type affects the individual BIQA feature in a distinct manner because each type of distortion exhibits different characteristics
Summary
Multimedia content has become a significant part of our lives. Delivery of images at the highest quality to the end user is an essential requirement for many modern imaging applications. Estimation of perceived image quality by humans, known as subjective evaluation has gained importance. Subjective evaluation is used as a benchmark for image quality assessment (IQA), but the constraint of time and the tedious nature of the task make it unsuitable for many applications [1]. IQA techniques aim to replicate the behavior of human visual system to evaluate the quality score of images using objective parameters or measures. Objective IQA is divided into full reference (FR), reduced reference (RR), and blind IQA techniques. FR-IQA techniques require the pristine version of the image to predict the quality score of images [2–10].
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