Image partial blur detection and classification

Bovik

2010

J Real-Time Image Proc

The development of real-time image and video quality assessment algorithms is an important direction on which little research has focused. Towards this end, we present a design of real-time implementable full-reference image/video quality algorithms that are based on the Structural SIMilarity (SSIM) index and multi-scale SSIM (MS-SSIM) index. The proposed algorithms, which modify SSIM/MS-SSIM to achieve speed of execution, were tested on the LIVE Image Quality Database and LIVE Video Quality Database. The experimental results show that the performance of the new, fast algorithms is commensurate with that of SSIM and MS-SSIM, but with much lower computational complexity. Indeed, the proposed Fast MS-SSIM algorithm is 10 times faster (lower complexity) than the MS-SSIM algorithm, while the proposed Fast SSIM is 2.68 times faster than SSIM without parallel computing optimization.

Section: Contrast and Structure Termsmentioning

confidence: 99%

Fast structural similarity index algorithm

Bovik

2010

J Real-Time Image Proc

“…Liu et al [10] and Levin [11] have demonstrated that the heavy tailed distributions of gradients can be used for blur detection. Liu et al used the gradient histogram span as a feature in their classification model.…”

Section: Natural Image Statisticsmentioning

confidence: 99%

“…In choosing features, instead of using the features mentioned in Liu et al [10] or Levin [11], we use the entire gradient histogram as the feature, which contains more information than just the mean or slope of the histogram.…”

Section: Probabilistic Svm For Blur Assessmentmentioning

confidence: 99%

No-reference image blur assessment using multiscale gradient

2009 International Workshop on Quality of Multimedia Experience

Bovik

2009

The increasing number of demanding consumer video applications, as exemplified by cell phone and other lowcost digital cameras, has boosted interest in no-reference objective image and video quality assessment (QA). In this paper, we focus on no-reference image and video blur assessment. There already exist a number of no-reference blur metrics, but most are based on evaluating the widths of intensity edges, which may not reflect real image quality in many circumstances. Instead, we consider natural scenes statistics and adopt multi-resolution decomposition methods to extract reliable features for QA. First, a probabilistic support vector machine (SVM) is applied as a rough image quality evaluator; then the detail image is used to refine and form the final blur metric. The algorithm is tested on the LIVE Image Quality Database; the results show the algorithm has high correlation with human judgment in assessing blur distortion of images.

“…Nevertheless, when only a part of an image is blurred, such a blurred region must be extracted in order to estimate the parameters of point spread function (PSF). Based on color, gradient, and spectrum features of a blurred region, such features are trained and then utilized to identify the blurred region [7]. In [8], a twostep strategy is developed for detecting and extracting the blurred region automatically.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration for Linear Local Motion-Blur Based on Cepstrum

2012 Sixth International Conference on Genetic and Evolutionary Computing

Zhang

Tseng

et al. 2012

This paper presents a real-time restoration method for linear local motion-blur image. For an image in which only the fast moving-object is blurred but the background is clear, the proposed basic strategy is to divide such an image into many subimages firstly and then detect the blurred subimage by the gradient distribution and the maximum of cepstrum. For a blurred subimage, the blur direction and blur length are estimated to calculate the parameters of point spread function (PSF) and Lucy-Richardson deconvolution algorithm is employed to restore this blurred subimage. Using many artificial and real blurred images, the experimental results show that the proposed approach is more accurate and robust than other methods.