Blur evaluation is an image quality assessment process used to estimate the perceived sharpness or blurriness of images that imaging systems or processing algorithms output. Blur evaluation has numerous uses in practical applications and plays a central role in shaping image acquisition

transmission

analysis algorithms and systems

as well as the implementation

optimization

and testing of these algorithms and systems. An image sparse representation and probabilistic latent semantic analysis (pLSA)-based blur evaluation method is proposed to incorporate the unsupervised learning characteristic and the hierarchical feature abstraction model of the human visual system to the blur evaluation process. The proposed method is based on the hypothesis that images possess latent characteristics that can be used to measure image quality and the fact that the human brain can learn in an unsupervised manner. The pLSA model is used to identify meaningful topics that are latent in the sparse codes of natural images and the test image. The similarity of the latent topics between training images and the test image is used to measure blurriness. The proposed method has three crucial stages

which are the dictionary construction

learning

and blur metric computation stages. The dictionary construction stage develops the dictionary from clear sample images. The learning stage extracts awerage topics from the sparse codes of clear training images by using pLSA. The blur metric computation stage calculates the blur metric according to the correlation coefficient between the latent topics of the test image and the awerage topics of the clear training images. Experimental results on the synthetic images and public image quality databases show that the proposed method exhibits better performance than the state-of-the-art blur metrics in terms of monotonicity

anti-noise capability

and the suggested evaluation metrics of the Video Quality Experts Group. The Pearson correlation and Spearman rank-order correlation coefficients are approximately 0.995 6 and 0.993 4

respectively. Based on the unsupervised learning characteristic and the hierarchical feature abstraction model of the human visual system

this study proposes a novel blur evaluation method. This method uses the similarity of latent topics between training images and the test image to measure blurriness. Experimental results show that the proposed blur evaluation method can evaluate the amount of blurriness in images with high accuracy

and it correlates well with the human visual system.