No-reference Blur Research Articles

No-Reference Blur Assessment Based on Re-Blurring Using Markov Basis

Blur is produced in a digital image due to low pass filtering, moving objects or defocus of the camera lens during capture. Image viewers are annoyed by blur artefact and the image's perceived quality suffers as a result. The high-quality input is relevant to communication service providers and imaging product makers because it may help them improve their processes. Human-based blur assessment is time-consuming, expensive and must adhere to subjective evaluation standards. This paper presents a revolutionary no-reference blur assessment algorithm based on re-blurring blurred images using a special mask developed with a Markov basis and Laplace filter. The final blur score of blurred images has been calculated from the local variation in horizontal and vertical pixel intensity of blurred and re-blurred images. The objective scores are generated by applying proposed algorithm on the two image databases i.e., Laboratory for image and video engineering (LIVE) database and Tampere image database (TID 2013). Finally, on the basis of objective and subjective scores performance analysis is done in terms of Pearson linear correlation coefficient (PLCC), Spearman rank-order correlation coefficient (SROCC), Mean absolute error (MAE), Root mean square error (RMSE) and Outliers ratio (OR). The existing no-reference blur assessment algorithms have been used various methods for the evaluation of blur from no-reference image such as Just noticeable blur (JNB), Cumulative Probability Distribution of Blur Detection (CPBD) and Edge Model based Blur Metric (EMBM). The results illustrate that the proposed method was successful in predicting high blur scores with high accuracy as compared to existing no-reference blur assessment algorithms such as JNB, CPBD and EMBM algorithms.

A No-Reference Blur Metric based on Second-Order Gradients of Image

Estimation of blurriness value in image is an important issue in image processing applications such as image deblurring. In this paper, a no-reference blur metric with low computational cost is proposed, which is based on the difference between the second order gradients of a sharp image and the one associated with its blurred version. The experiments, in this paper, performed on four databases, including CSIQ, TID2008, IVC, and LIVE. The experimental results indicate the capability of the proposed blur metric in measuring image blurriness, also the low computational cost, comparing with other existing approaches.

A no-reference framework for evaluating video quality streamed through wireless network

In this work, a no-reference framework is proposed for the video quality estimation streamed through the wireless network. The work presents a comprehensive survey of the existing full reference (FR), reduced reference (RR), and no-reference (NR) algorithms. A comparison has been made among existing algorithms, i.e. in terms of subjective correlation and feasibility to use these algorithms in wireless architecture, to describe the necessity of the proposed framework to overcome the limitations of the existing algorithms. A brief summary of our previously published algorithms, i.e. NR blockiness, NR blur, NR network, NR just noticeable distortion, and RR, has also been presented. These algorithms have also been used as function modules in the proposed framework. The proposed framework is able to measure the video quality by taking into account major spatial, temporal, network impairments, and human visual system effects for a comprehensive quality evaluation. The proposed framework is able to measure the video quality compressed by different codecs, i.e. MPEG x / H.264x, Motion JPEG/Motion, and JPEG2000, etc. The framework is able to work with two different kinds of received data, i.e. bit streams and decoded pixels. The framework is an integration of the RR and NR method, and can work in three different modes depending on the availability of the RR data, i.e. 1) only RR measurement, 2) hybrid of RR and NR measurement, and 3) only NR estimation. In addition, any individual function block, i.e. blurring, can also be used independently for particular specific distortion. A new subjective video quality database containing compressed and distorted videos (due to channel induced distortions) is also developed to test the proposed framework. The framework has also been tested on publicly available LIVE Video Quality Database. Overall test results show that our framework demonstrates a strong correlation with subjective evaluation of the two separate video databases as compared with other existing algorithms. The proposed framework also shows good results while working only in NR mode as compared with existing RR and FR algorithms. The proposed framework is more scalable and feasible to use in any kind of available network bandwidth as compared with other algorithms, as it can be used in different modes by using different function modules.

A No-reference Blur Image Quality Assessment Algorithm Based on Wavelet Singular Value Decomposition

A simple and effective no reference blur image quality assessment algorithm based on wavelet high frequency singular value decomposition is proposed. As the different wavelet high frequency sub-bands in the same level are highly structural correlation, and the degree of correlation would be weaken as the degree of blur distortion strengthen. The proposed method decomposes the images by wavelet transform firstly. Then the singular value decomposition is used for different high frequency sub-bands to get their singular value vectors, which we used to represent their structural information. Thirdly the angles between different sub-bands singular value vectors are computed, which reflects their degree of correlation. Finally the sum of angles is used as the last objective assessment index. Compared to the traditional methods, the proposed algorithm is more efficient and practical as it does not need to train or create a reference image. Experimental results show its good effectiveness and performance on LIVE2, CSIQ and TID2013 databases.

Interview

Professor Mohamed Deriche of King Fahd University, Saudi Arabia, talks to us about the work behind the paper ‘Quantifying blur in colour images using higher order singular values’, page 1755 Professor Mohamed Deriche Digital images and videos are becoming an essential part of our quality of experience, and they provide a source of information for various social and economic aspects of society. Every minute, more than 3 million videos are viewed on YouTube, over 500,000 photos are posted online, and more than 20 million messages are exchanged on WhatsApp, many of which contain images. Unfortunately, with this substantial amount of internet traffic, and with a lack of control of content, the quality of images and videos posted suffers the most. During different processing stages (acquisition, transmission, compression, etc.), various kinds of artefacts are introduced. Among these, blur is the most commonly observed distortion, which appears due to the limitations of acquisition equipment (like out-of-of focus camera lens, low-lighting conditions, relative movement) and different processing stages before final viewing by the user. More importantly, blur affects edge information which is key to human perception of quality. In this Letter, we have introduced a no-reference blur assessment technique for colour images. The spatial and inter-channel correlations in colour images are exploited to quantify blur efficiently, rather than using the traditional luminance component only, or the individual colour channels in existing techniques. A colour image is considered as a 3rd-order tensor and is decomposed into different 2D matrices or unfoldings. The higher-order singular values are calculated for these unfoldings using conventional SVD. We show that the singular values in these unfoldings follow an exponentially decreasing curve, whereas the degree of exponent varies with the amount of blur, and hence is used as an objective quality score. The proposed blur metric was tested on distorted images from various publicly available databases, and the results validate its superiority and effectiveness compared to state-of-the-art blur assessment methods. The proposed technique could be embedded in camera sensors to provide photo-after-blur removal, and could be used in multimedia applications for best quality of experience delivery of videos to the end users. We also mentioned above adding the proposed work as a feature in different applications and systems providing multimedia content online. Quality of experience in the multimedia industry is driving technology to its best. The move towards more virtual reality applications and systems will substantially benefit from validated research work in objectively quantifying different manipulation effects on original content. Here, we are working with blur which is an important component in the spectrum of distortions, but the work can be extended to quantifying enhancement (such as retouching in the entertainment industry), or even expanding it to related applications such as image forging, watermarking, content, authentication, etc. This DSP research group within the EE Department at KFUPM is actively involved in developing new algorithms and systems for different applications in multimedia. These include: image and video quality assessment, compression, content retrieval, transmission, biometrics, crowd control, sign language recognition, etc. The work presented here has been developed in collaboration with Prof. Azeddine Beghdadi, the director of the L2TI Research Lab. at Univ. Paris 13. Special thanks and acknowledgements go to my student, Mr. Muhammad Qureshi, who implemented the algorithms developed here, tested these, and compared their performance to state-of-the-art techniques.

Blind image quality assessment for Gaussian blur images using exact Zernike moments and gradient magnitude

Features that exhibit human perception on the effect of blurring on digital images are useful in constructing a blur image quality metric. In this paper, we show some of the exact Zernike moments (EZMs) that closely model the human quality scores for images of varying degrees of blurriness can be used to measure these distortions. A theoretical framework is developed to identify these EZMs. Together with the selected EZMs, the gradient magnitude (GM), which measures the contrast information, is used as a weight in the formulation of the proposed blur metric. The design of the proposed metric consists of two stages. In the first stage, the EZM differences and the GM dissimilarities between the edge points of the test image and the same re-blurred image are extracted. Next, the mean of the weighted EZM features are then pooled to produce a quality score using support vector machine regressor (SVR). We compare the performance of the proposed blur metric with other state-of-the-art full-reference (FR) and no-reference (NR) blur metrics on three benchmark databases. The results using Pearson׳s correlation coefficient (CC) and Spearman׳s ranked-order correlation coefficient (SROCC) for the LIVE image database are 0.9659 and 0.9625 respectively. Similarly, high correlations with the subjective scores are achieved for the other two databases as well.

A noise-immune no-reference metric for estimating blurriness value of an image

Blur is a type of distortion that may happen in digital images. Blur estimation is an important issue in image processing applications such as image deblurring and depth estimation. Several blur metrics exist in the literature, but they are mostly sensitive to the presence of noise. In this paper, a simple yet accurate no-reference blur metric with low computational cost is proposed, which is robust against noise. The proposed blur metric is based on the observation that there is a considerable difference between the DCT of a sharp image and the one associated with its blurred version. The effect of noise is mainly reflected in the higher order DCT coefficients. Hence, the noise effect is mitigated in this paper via discarding the higher order DCT coefficients. The experiments, performed on four databases (including CSIQ, TID2008, IVC, and LIVE), indicate the capability of the proposed metric in measuring image blurriness. Comparative results with other existing approaches show the superiority of the proposed blur metric, especially at the presence of noise.

Perceived no reference image quality measurement for chromatic aberration

Today there is need for no reference (NR) objective perceived image quality measurement techniques as conducting subjective experiments and making reference image available is a very difficult task. Very few NR perceived image quality measurement algorithms are available for color distortions like chromatic aberration (CA), color quantization with dither, and color saturation. We proposed NR image quality assessment (NR-IQA) algorithms for images distorted with CA. CA is mostly observed in images taken with digital cameras, having higher sensor resolution with inexpensive lenses. We compared our metric performance with two state-of-the-art NR blur techniques, one full reference IQA technique and three general-purpose NR-IQA techniques, although they are not tailored for CA. We used a CA dataset in the TID-2013 color image database to evaluate performance. Proposed algorithms give comparable performance with state-of-the-art techniques in terms of performance parameters and outperform them in terms of monotonicity and computational complexity. We have also discovered that the proposed CA algorithm best predicts perceived image quality of images distorted with realistic CA.

No-reference blur assessment based on edge modeling

This paper presents a no-reference objective blur metric based on edge model (EMBM) to address the image blur assessment problem. A parametric edge model is incorporated to describe and detect edges, which can offer simultaneous width and contrast estimation for each edge pixel. With the pixel-adaptive width and contrast estimations, the probability of detecting blur at edge pixels can be determined. Also, unlike previous work, we advocate using only the salient edge pixels to simulate the blur assessment in Human Visual System (HVS). Finally, the blur metric is obtained by cumulating the probability of blur detection. Various images with different blur distortions are tested to demonstrate the effectiveness of the proposed metric.

No-reference image blur index based on singular value curve

We describe a new no-reference blur index for still images based on a singular value curve (SVC). The algorithm is composed of two steps. First, the singular value decomposition is performed on the image to be blur-assessed. Then an image blur index is constructed from the singular value curve. Experimental results obtained on four simulated blur databases and on the Real Blur Image Database show that the proposed SVC algorithm achieves high correlation against human judgments when assessing the blur distortion of images.

A Blind Blur Detection Scheme Using Statistical Features of Phase Congruency and Gradient Magnitude

The growing uses of camera-based barcode readers have recently gained a lot of attention. This has boosted interest in no-reference blur detection algorithms. Blur is an undesirable phenomenon which appears as one of the most frequent causes of image degradation. In this paper we present a new no-reference blur detection scheme that is based on the statistical features of phase congruency and gradient magnitude maps. Blur detection is achieved by approximating the functional relationship between these features using a feed forward neural network. Simulation results show that the proposed scheme gives robust blur detection scheme.

Study Algorithms which Assessed Quality of the Blurred Images

No-reference measurement of blurring artifacts in images is a difficult problem in image quality assessment field. In this paper, we present a no-reference blur metric to measure the quality of the images. These images are degraded using Gaussian blurring. Suggestion method depends on developing the Mean of Locally Standard deviation and Mean of the image (MLSD) model, this method is called Blur Quality Metric (BQM) and it calculates from numerical integral of the function in this model. And the BQM is compared with the No-reference Perceptual Blur Metrics (PBM) and the Entropy of the First Derivative (EFD) Image; the BQM is a simple metric and gives good accuracy in metrics the quality for the Gaussian blurred image if it compared with another algorithms.

A Novel No-Reference Perceptual Blur Metric

In this paper, we present a novel no-reference blur metric for images. The blur metric is based on analyzing image features include the mean value of phase congruency image, the entropy of phase congruency image and the distorted image, and the gradient of the distorted image. The new index does NOT need any information from reference image, and image quality estimation is accomplished by simple functional relationship between those features. Our experimental results show that the new index outperforms existing popular no-reference blurriness metric and full reference PSNR on LIVE Gaussian blurred database and IVC blurring images.

A SURVEY ON EFFICIENT NO-REFERENCE BLUR ESTIMATION METHODS

Blur estimation in image processing has come to be of great importance in assessing the quality of ima ges. This work presents a survey of no-reference blur estimation methods. A n o-reference method is particularly useful, when the input image used for blur estimation does not have an available corresponding reference image. This paper provides a comparison of the methodologies of four no-reference blur estimation methods. The first met hod applies a scale adaptive technique of blur esti mation to get better accuracy in the results. The second blur metric involves findin g the energy using second order derivatives of an i mage using derivative pair of quadrature filters. The third blur metric is based on the kurtosis measurement in the discrete dyadic wavelet transform (DDWT) of the images. The fourth method of blur estimation is obt ained by finding the ratio of sum of the edge width s of all the detected edges to the total number of edges. The results provided are use ful in comparing the methods based on metrics like Spearman correlation coefficient. The results are obtained by evaluation on images from the Laboratory for Image and Video Engineering (LIVE) database. The various methods are evaluated on the images by adding varying content of noise. The performance is evaluated for 3 different categories namely Gaussian blur, motion blur and al so JPEG2000 compressed images. Blur estimation fin ds its application in quality assessment, image fusion and auto-focusing in image s. The sharpness of an image can also be found from the blur metric as sharpness is inversely proportional to blur. Sharpness metric s can also be combined with other metrics to measur e overall quality of the image.

No-reference blur image quality measure based on multiplicative multiresolution decomposition

A new approach for analyzing the blur effect on real images is proposed. This approach is based on the Multiplicative Multi-resolution Decomposition MMD. From MMD image-content analysis, a blind image quality measure dedicated to blur is then derived. The proposed measure has been applied on Gaussian-blurred and JPEG2000-compressed images from the LIVE, TID and IVC databases. The performance of the proposed measure is evaluated and compared with some referenced image quality metrics. The experimental results measured in terms of correlation with the subjective assessment of the images, demonstrate the efficiency of the proposed image quality measure in predicting the amount of blur.

No-Reference Blur Assessment Based on Entropy and PCNN for Restoration Images

In practical problems, there are usually no clear counterparts as reference to evaluate restoration results. So no-reference blur assessment is very important and necessary. In this paper, we proposed an objective measure named as Edge Factor (EF) to appraise image blurring. The fundamental rationale was that blurring effect was much more perceptible in edge transition zones. The pixel number of edge transition zones would decrease when blurring occured. We defined the pixel number ratio of the edge transition zones to the whole image as EF. Experimental results show the monotonic consistency of EF and RMS. The proposed method is further compared with some common edge detection algorithms to demonstrate the effectiveness of combining point-based entropy with Pulse Coupled Neural Network.

Full-Reference and No-reference Image Blur Assessment Based on Edge Information

Blur images are often subjected to the loss of high frequency content during acquisition, compression and multimedia transmission. Hence, objective blur assessment is implemented to identify and quantify image quality degradation by blurriness artifact in order to maintain and control the quality of the images. In this paper, objective full-reference and no-reference blur assessments using edge information are presented with the aim to provide computational models that can automatically measure the amount of blurriness artifact such as Gaussian blur on the images. The amount of Gaussian blur on an image, also known as the final blur measurement is determined by averaging the sum of edge width over all detected edges which satisfy the edge criteria. The final blur measurement for all test images based on full-reference and no-reference implementations are also validated with subjective results. The validation results show that the objective full-reference and no-reference blur assessments correlate closely to perceptual image quality.

No-Reference Blur Assessment of Digital Pictures Based on Multifeature Classifiers

In this paper, we address the problem of no-reference quality assessment for digital pictures corrupted with blur. We start with the generation of a large real image database containing pictures taken by human users in a variety of situations, and the conduction of subjective tests to generate the ground truth associated to those images. Based upon this ground truth, we select a number of high quality pictures and artificially degrade them with different intensities of simulated blur (gaussian and linear motion), totalling 6000 simulated blur images. We extensively evaluate the performance of state-of-the-art strategies for no-reference blur quantification in different blurring scenarios, and propose a paradigm for blur evaluation in which an effective method is pursued by combining several metrics and low-level image features. We test this paradigm by designing a no-reference quality assessment algorithm for blurred images which combines different metrics in a classifier based upon a neural network structure. Experimental results show that this leads to an improved performance that better reflects the images' ground truth. Finally, based upon the real image database, we show that the proposed method also outperforms other algorithms and metrics in realistic blur scenarios.

No-reference blur index using blur comparisons

Presented is a new no-reference blur index for still images that is based on the observation that it can be difficult to perceive between versions of an image blurred to different degrees. A ‘re-blurred’ image is produced by intentionally blurring the test image. Local sample statistics are computed in the vicinity of detected edges of the original and re-blurred images, respectively. These are differenced and normalised to construct a new blur index. Experimental results on four simulated blur databases and on the Real Blur Image Database show that the proposed method obtains high correlations with test subjective quality evaluations.

Objective no-reference image blur metric based on local phase coherence

A novel algorithm for no-reference blur assessment in digital pictures is proposed. This metric is based on the evaluation of local phase coherence across the scales of an overcomplete wavelet transform. We compare the performance of the proposed method with four state-of-the art metrics using a large database containing both simulated and real blur.