RECENT DEVELOPMENTS IN IMAGE QUALITY ASSESSMENT ALGORITHMS: A REVIEW

Image Quality Assessment (IQA) has become a subject of intense research interest in the recent years. The demand for accurate, consistent, computationally simple and easy-to-use quality assessment tools that can be used to measure, control, and improve the perceptual quality of images and video is increasing day by day. Applications of IQA include machine vision, medical imaging, multimedia communication, entertainment and other image processing activities. Systems embedded with IQA algorithms can replace humans for evaluating image quality in real-time applications and hard-to-reach environments. As most of the images are ultimately viewed by human observers, the best method to assess the quality of an image is by subjective tests by human observers. However, subjective tests are expensive, time consuming and difficult to perform in real-time applications. Therefore, these tests are done objectively using computer algorithms. These algorithms attempt to evaluate the quality of the image in the same way as how humans perceive image quality. In this article we present an up-to-date review on IQA research and its future trends, the principles and methodologies used in popular Full Reference IQA algorithms, the methodologies and parameters used for evaluating the performance of IQA algorithms and performance comparison of important IQA algorithms.

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