No-reference image blur assessment using multiscale gradient

Ming-Jun Chen,Alan C Bovik

doi:10.1186/1687-5281-2011-3

Abstract

The increasing number of demanding consumer video applications, as exemplified by cell phone and other low-cost digital cameras, has boosted interest in no-reference objective image and video quality assessment (QA) algorithms. In this paper, we focus on no-reference image and video blur assessment. We consider natural scenes statistics models combined with multi-resolution decomposition methods to extract reliable features for QA. The algorithm is composed of three steps. First, a probabilistic support vector machine (SVM) is applied as a rough image quality evaluator. Then the detail image is used to refine the blur measurements. Finally, the blur information is pooled to predict the blur quality of images. The algorithm is tested on the LIVE Image Quality Database and the Real Blur Image Database; the results show that the algorithm has high correlation with human judgments when assessing blur distortion of images.

Highlights

With the rapid and massive dissemination of digital images and videos, people live in an era replete with digitized visual information
We are mainly concerned with NR blur assessment, which remains an important problem in many applications
This result is consistent with our other work in FR quality assessment (QA), where we have found that mid-band QA scores tend to score higher than low-band or high-band scores

Summary

Introduction

With the rapid and massive dissemination of digital images and videos, people live in an era replete with digitized visual information. Different pooling rules were applied on the blurred image portion of the LIVE Image Quality Database scores and human subjectivity for each layer. When comparing the performance of our proposed algorithm with other blur assessment algorithms, we refer to the work conducted by Ciancio et al [20] In this work, they provided performance levels several algorithms, including a frequency-domain blur index [14], a wavelet-based blur index [15], a perceptually motivated blur index [7], a blur index using a human visual system (HVS) model [11], a local phase coherence blur metric [16], and their own Multi-Features Neural Network Classifier (MFNNC) blur metric [20]. Content whose interpretation may depend on the observers’ preferences regarding composition (and that of the photographer)

Conclusion

21. Simoncelli EP

Findings

26. Levin A