A Novel Probabilistic Latent Semantic Analysis Based Image Blur Metric

The proposed metric is to use latent quality aware topics in an image to measure blurriness. A novel image quality vocabulary is firstly obtained by the contrast features computed from the training images using K-means. Probabilistic latent semantic analysis model is then used to discover quality aware topics that are latent in clear sample images and the test image. The similarity between the latent topics of the test image and the average topics of clear images is finally computed to measure blurriness. Experimental results show that the proposed blur metric is monotonic, robust to additive noises, and also consistent with the human visual system.