No-reference blur index using blur comparisons

Li, C.; Wen-long, Yuan; Bovik, Alan C.; Wu, Xiaojun

doi:10.1049/el.2011.0921

Cited by 36 publications

(21 citation statements)

References 6 publications

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“…Some methods are developed to predict the perceptual quality of images that have suffered from specific kind of distortions. For example, methods for detecting perceptual quality of blurred images [6], [7], JPEG2000-compressed images [8], [9] are application-specific BIQA techniques. When the type of distortion is already known, these techniques can be readily used.…”

Section: Previous Workmentioning

confidence: 99%

Utilizing Image Scales Towards Totally Training Free Blind Image Quality Assessment

Saha

2015

IEEE Trans. on Image Process.

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A new approach to blind image quality assessment (BIQA), requiring no training, is proposed in this paper. The approach is named as blind image quality evaluator based on scales and works by evaluating the global difference of the query image analyzed at different scales with the query image at original resolution. The approach is based on the ability of the natural images to exhibit redundant information over various scales. A distorted image is considered as a deviation from the natural image and bereft of the redundancy present in the original image. The similarity of the original resolution image with its down-scaled version will decrease more when the image is distorted more. Therefore, the dissimilarities of an image with its low-resolution versions are cumulated in the proposed method. We dissolve the query image into its scale-space and measure the global dissimilarity with the co-occurrence histograms of the original and its scaled images. These scaled images are the low pass versions of the original image. The dissimilarity, called low pass error, is calculated by comparing the low pass versions across scales with the original image. The high pass versions of the image in different scales are obtained by Wavelet decomposition and their dissimilarity from the original image is also calculated. This dissimilarity, called high pass error, is computed with the variance and gradient histograms and weighted by the contrast sensitivity function to make it perceptually effective. These two kinds of dissimilarities are combined together to derive the quality score of the query image. This method requires absolutely no training with the distorted image, pristine images, or subjective human scores to predict the perceptual quality but uses the intrinsic global change of the query image across scales. The performance of the proposed method is evaluated across six publicly available databases and found to be competitive with the state-of-the-art techniques.

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Section: Previous Workmentioning

confidence: 99%

Utilizing Image Scales Towards Totally Training Free Blind Image Quality Assessment

Saha

2015

IEEE Trans. on Image Process.

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“…The first category is composed of methods that try to model the distortion such as blur [8][9][10] or blockiness. 11 These methods are application specific and are not in the scope of this work.…”

Section: Nr-iqamentioning

confidence: 99%

Study of no-reference image quality assessment algorithms on printed images

Eerola

Lensu

Kälviäinen

et al. 2014

J. Electron. Imaging

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Abstract. Measuring the visual quality of printed media is important since printed products have an important role in everyday life. Finding ways to automatically predict the image quality has been an active research topic in digital image processing, but adapting those methods to measure the visual quality of printed media has not been studied often or in depth and is not straightforward. Here, we analyze the efficacy of no-reference image quality assessment (IQA) algorithms originally developed for digital IQA with regards to predicting the perceived quality of printed natural images. We perform a comprehensive statistical comparison of the methods. The best methods are shown to accurately predict subjective opinions of the quality of printed photographs using data from a psychometric study.

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“…As a result, Crete et al make use of this character of human eyes to assess the blur of an image. [5,7].…”

Section: A a New No Reference Perceptual Image Blur Metricmentioning

confidence: 99%

The new improved image blur assessment metrics

Liu

Yang

Zhang

et al. 2014

2014 7th International Congress on Image and Signal Processing

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Image blur assessment has always played an important role in the image quality assessment (IQA). In fact, many methods have been used to measure the blur of an image, but we find that lots of the traditional methods can't effectively compare the blur among images with different contents, leading to the blur measuring more difficult. Recently, we find a perfect method based on the idea that it is difficult for us to find the differences between an already blurred picture and a re-blurred picture based on the former. As a result, Crete et al. use this character of human eyes to assess the blur of an image. and study it carefully. By analyzing the method, though it's good enough, we find some shortcomings of it and propose two improved algorithms based on it: a new blur metric based on the variation of the neighboring differences around edge and a blur metric based on neighboring variation of second difference matrix. The two new methods have been proved that they are all improved to some extent compared with the original algorithm by experiments. Being helpful to the blur assessment, the improved methods are better in veracity, consistency and monotonousness. What's more, they provide a novel perspective to develop blur metrics.

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No-reference blur index using blur comparisons

Cited by 36 publications

References 6 publications

Utilizing Image Scales Towards Totally Training Free Blind Image Quality Assessment

Utilizing Image Scales Towards Totally Training Free Blind Image Quality Assessment

Study of no-reference image quality assessment algorithms on printed images

The new improved image blur assessment metrics

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