A No-Reference Image Quality Measure for Blurred and Compressed Images Using Sparsity Features

Abstract

Images can be distorted in the real world via many sources like faulty sensors, artifacts generated by compression algorithms, defocus, faulty lens, and poor lighting conditions. Our biological vision system can identify the quality of image by looking at the images, but developing an algorithm to assess the quality of an image is a very challenging task as an image can be corrupted by different types of distortions and statistical properties of different types of distortions are dissimilar. The main objective of this article is to propose an image quality assessment technique for images corrupted by blurring and compression-based artifacts. Machine learning-based approaches have been used in recent times to perform this task. Images can be analyzed in different transform domains like discrete cosine transform domain, wavelet domains, curvelet domains, and singular value decomposition. These domains generate sparse matrices. In this paper, we propose no-reference image quality assessment algorithms for images corrupted by blur and different compression algorithms using sparsity-based features computed from different domains and all features pooled by support vector regression. The proposed model has been tested on three standard image quality assessment datasets LIVE, CSIQ, and TID2013, and correlation with subjected human opinion scores has been presented along with comparative study with state-of-the-art quality measures. Experiments run on standard image quality databases show that the results obtained are outperforming the existing results.