No-reference visually significant blocking artifact metric for natural scene images

Suthaharan, Shan

doi:10.1016/j.sigpro.2009.02.007

Cited by 73 publications

(29 citation statements)

References 12 publications

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“…Most existing blind IQA models proposed in the past assume that the image whose quality is being assessed is afflicted by a particular kind of distortion [5]- [11], [17]. These approaches extract distortion-specific features that relate to loss of visual quality, such as edge-strength at blockboundaries.…”

Section: Previous Workmentioning

confidence: 99%

“…By quantifying natural image statistics and refraining from an explicit characterization of distortions, our approach to quality assessment is not limited by the type of distortions that afflict the image. Such approaches to NR IQA are significant since most current approaches are distortion-specific [5]- [11], i.e., they are capable of performing blind IQA only if the distortion that afflicts the image is known beforehand, e.g., blur or noise or compression and so on (see below). Previously, we have proposed other NSS-based distortion-generic approaches to NR IQA that statistically model images in the wavelet domain [12] and in the DCT-domain [13].…”

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confidence: 99%

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No-Reference Image Quality Assessment in the Spatial Domain

Mittal

Moorthy

Bovik

2012

IEEE Trans. on Image Process.

4,512

2,405

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Abstract-We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of "naturalness" in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-tonoise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http:// live.ece.utexas.edu/ research/ quality/ BRISQUE_release.zip for public use and evaluation.Index Terms-Blind quality assessment, denoising, natural scene statistics, no reference image quality assessment, spatial domain.

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Section: Previous Workmentioning

confidence: 99%

mentioning

confidence: 99%

No-Reference Image Quality Assessment in the Spatial Domain

Mittal

Moorthy

Bovik

2012

IEEE Trans. on Image Process.

4,512

2,405

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“…A weighted Sobel operator-based blocking method is presented in [53], in which the computation involves luminance gradient matrices of DCT-coded images. A method where a rather simple approach of taking abrupt change in pixel values as a signal of blocking has been proposed in [54] and it can be implemented both in pixel and DCT domain, and a method of blockiness estimation in natural scene JPEG compressed images has been presented in [55] which was influenced by the impact of multineural channels pattern of HVS for vision sensing.…”

Section: Blockingmentioning

confidence: 99%

No-reference image and video quality assessment: a classification and review of recent approaches

et al. 2014

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The field of perceptual quality assessment has gone through a wide range of developments and it is still growing. In particular, the area of no-reference (NR) image and video quality assessment has progressed rapidly during the last decade. In this article, we present a classification and review of latest published research work in the area of NR image and video quality assessment. The NR methods of visual quality assessment considered for review are structured into categories and subcategories based on the types of methodologies used for the underlying processing employed for quality estimation. Overall, the classification has been done into three categories, namely, pixel-based methods, bitstream-based methods, and hybrid methods of the aforementioned two categories. We believe that the review presented in this article will be helpful for practitioners as well as for researchers to keep abreast of the recent developments in the area of NR image and video quality assessment. This article can be used for various purposes such as gaining a structured overview of the field and to carry out performance comparisons for the state-of-the-art methods. Keywords:No-reference; Image quality assessment; Video quality assessment; Perceptual quality 1 Review IntroductionThere has been a tremendous progress recently in the usage of digital images and videos for an increasing number of applications. Multimedia services that have gained wide interest include digital television broadcasts, video streaming applications, and real-time audio and video services over the Internet. The global mobile data traffic grew by 81% in 2013, and during 2014, the number of mobileconnected devices will exceed the number of people on earth, according to predictions made by Cisco. The video portion of the mobile data traffic was 53% in 2013 and is expected to exceed 67% by 2018 [1]. With this huge increase in the exposure of image and video to the human eye, the interest in delivering quality of experience (QoE) may increase naturally. The quality of visual media can get degraded during capturing, compression, transmission, reproduction, and displaying due to the distortions that might occur at any of these stages. The legitimate judges of visual quality are humans as end users, the opinions of whom can be obtained by subjective experiments. Subjective experiments involve a panel of participants which are usually non-experts, also referred to as test subjects, to assess the perceptual quality of given test material such as a sequence of images or videos. Subjective experiments are typically conducted in a controlled laboratory environment. Careful planning and several factors including assessment method, selection of test material, viewing conditions, grading scale, and timing of presentation have to be considered prior to a subjective experiment. For example, Recommendation (ITU-R) BT.500 [2] provides detailed guidelines for conducting subjective experiments for the assessment of quality of television pictures. The outcomes of a subjectiv...

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“…Several NR methods have been proposed [3][4][5][6][7][8][9][10][11][12][13][14][15]. These NR methods mainly measure blocking and blurring artifacts.…”

Section: Introductionmentioning

confidence: 99%

“…Wu et al proposed a blocking metric (generalized block impairment metric (GBIM)), which employed a texture and luminance masking method to weight a blocking feature [3]. In [7,8], blocking metrics were developed to measure the blockiness between adjacent block edge boundaries. However, these methods do not consider that the visibility can be changed depending on background luminance levels.…”

Section: Introductionmentioning

confidence: 99%

No-reference image quality metric based on image classification

Choi

Lee

2011

EURASIP J. Adv. Signal Process.

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In this article, we present a new no-reference (NR) objective image quality metric based on image classification. We also propose a new blocking metric and a new blur metric. Both metrics are NR metrics since they need no information from the original image. The blocking metric was computed by considering that the visibility of horizontal and vertical blocking artifacts can change depending on background luminance levels. When computing the blur metric, we took into account the fact that blurring in edge regions is generally more sensitive to the human visual system. Since different compression standards usually produce different compression artifacts, we classified images into two classes using the proposed blocking metric: one class that contained blocking artifacts and another class that did not contain blocking artifacts. Then, we used different quality metrics based on the classification results. Experimental results show that each metric correlated well with subjective ratings, and the proposed NR image quality metric consistently provided good performance with various types of content and distortions.

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No-reference visually significant blocking artifact metric for natural scene images

Cited by 73 publications

References 12 publications

No-Reference Image Quality Assessment in the Spatial Domain

No-Reference Image Quality Assessment in the Spatial Domain

No-reference image and video quality assessment: a classification and review of recent approaches

No-reference image quality metric based on image classification

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