Goal oriented image quality assessment

Abstract

Abstract The area of image quality assessment(IQA) is an active research area in image processing and computer vision. All IQA algorithms reported in literature are attempting to quantify only the visual quality of the images/videos. An interesting question to be answered is that given a goal (task) and an image (with good visual quality), is the image good to achieve the goal by the best possible algorithm. In an attempt to spur the research community to answer this question, a new paradigm of IQA, called goal oriented IQA (GO‐IQA), is introduced. GO‐IQA is defined as given an image and a goal, predicting how good the image is to achieve that goal by the best possible algorithm. The need for GO‐IQA is that if GO‐IQA score is less, then any arbitrary algorithm attempting to achieve the goal will eventually fail. In this paper, considering segmentation as goal, a GO‐IQA algorithm is proposed to predict how good the image is to do accurate segmentation by the best possible segmentation algorithm. A support vector regression model has been trained with features related to image segmentation along with the accuracies of a class of well‐known image segmentation algorithms. In the process, we have obtained encouraging results in terms of the Pearson linear correlation coefficient and mean squared error. The predictions given by the proposed model have been validated with the scores of state of the art segmentation algorithm.