Multiscale contrast similarity deviation: An effective and efficient index for perceptual image quality assessment

• Generally, the effectiveness, namely, high correlation with the human subjective score, is the prerequisite of a good IQA model, it is of first importance to a IQA model.
• The efficiency (the least computation cost), however, is the second requirement of a good IQA model which become important under the premise that an IQA model meet the condition of "effectiveness".
• In this paper, we developed an effective and efficient IQA model called multiscale contrast similarity deviation (MCSD) which explores the contrast features by resorting to multiscale representation.
• Performances on six benchmark databases demonstrate its effectiveness and efficiency.

Perceptual image quality assessment (IQA) uses a computational model to assess the image quality in a fashion consistent with human opinions. A good IQA model should consider both the effectiveness and efficiency. To meet this need, a new model called multiscale contrast similarity deviation (MCSD) is developed in this paper. Contrast is a distinctive visual attribute closely related to the quality of an image. To further explore the contrast features, we resort to the multiscale representation. Although the contrast and the multiscale representation have already been used by other IQA indices, few have reached the goals of effectiveness and efficiency simultaneously. We compared our method with other state-of-the-art methods using six well-known databases. The experimental results showed that the proposed method yielded the best performance in terms of correlation with human judgments. Furthermore, it is also efficient when compared with other competing IQA models.