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It is challenging to quantitatively assess image quality in real time without a reference image while achieving human-level perception performance. In this paper, we present a no-reference (NR) image quality assessment (IQA) method called BNB (an acronym for blurriness, noisiness, and blockiness). Our BNB method quantifies the blurriness, noisiness, and blockiness of a given image, which are considered as three critical factors affecting users’ quality of experience. This method is rooted in the observation that for any image, the difference between any two adjacent pixel values follows a generalized Laplace distribution with zero mean. This Laplace distribution changes differently when the image experiences various types of artifacts, i.e., blurriness, noisiness, and blockiness. To construct a metric for each BNB artifact, we first extract features for each type of artifacting from the changing Laplace distribution and then identify the quantitative relationship between the feature value and the variation of the artifact. Given human perception scores of a popular image database, we use the k-nearest neighbor algorithm to map our three BNB metrics of an image to a human perception score. Experimental results reveal that the image quality score obtained from our BNB method has higher correlation with human perceptual scores in addition to requiring notably less computation compared with existing NR IQA methods. [ABSTRACT FROM PUBLISHER]