A no-reference perceptual blur metric

Abstract

In this paper, we present a no-reference blur metric for images and video. The blur metric is based on the analysis of the spread of the edges in an image. Its perceptual significance is validated through subjective experiments. The novel metric is near real-time, has low computational complexity and is shown to perform well over a range of image content. Potential applications include optimization of source coding, network resource management and autofocus of an image capturing device.

Perceptual blur and ringing metrics: application to JPEG2000

“…In the lossy JPEG2000 compression scheme [8,11] for example, the standard filter used for the wavelet decomposition is the Daubechies (9,7). Since the decomposition is done in a separable manner, i.e., first on the rows and then on the columns, it suffices to show the effect of these filters in 1D.…”

“…In the full-reference blur metric, we use the edges of the original image to determine the edge locations. For the no-reference blur metric, the edges are obtained directly from the processed/ compressed image [7]. While this affects the precision of edge detection to a certain extent (depending on the amount of compression or distortion), it is still possible to achieve good correlations with perceived blur, as will be shown in Section 3.…”

“…In Section 2, we illustrate the origins of blur and ringing artifacts. We then describe the perceptual blur metric, which was initially defined in [7], and use it in the design of a new ringing metric. In Section 3, we validate each of our perceptual metrics via subjective experiments and analyze the agreement between the metrics' predictions and the observer ratings.…”

No-Reference Image Quality Assessment through SIFT Intensity

“…Common NR metrics include variance metric, auto-correlation metric, frequency threshold metric, histogram threshold metric, histogram frequency metric, generalized block-edge impairment metrics, etc. [15,16,17,18,19,20,21,22,23,24]. Distortions can be mainly classified into noise, blur, blocking artifact (including blocking blur).…”

“…Distortions can be mainly classified into noise, blur, blocking artifact (including blocking blur). Many NR metrics can only measure some types of distortions, especially feasible for blocking or blur [15,16,17,18,19,20,21,22]. In [23], a natural scene statistics (NSS) model of contourlet coefficients adopts an image-dependent threshold to assess different distortions, but its accuracy still need to be further improved and it needs a training database to determine proper related parameters of the approach.…”