Seven challenges for image quality research

Chandler, Damon M.; Alam, Mushfiqul; Phan, Thien D.

doi:10.1117/12.2047117

Cited by 9 publications

(9 citation statements)

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“…An alternative to DS methods is the distortion-generic (DG) methods, which do not require a prior knowledge of the type of distortion and, therefore, are more adequate for diverse scenarios. As expected, the design of DG methods is more challenging [29,30].…”

Section: Introductionmentioning

confidence: 61%

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

et al. 2018

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Automatic assessing the quality of an image is a critical problem for a wide range of applications in the fields of computer vision and image processing. For example, many computer vision applications, such as biometric identification, content retrieval, and object recognition, rely on input images with a specific range of quality. Therefore, an effort has been made to develop image quality assessment (IQA) methods that are able to automatically estimate quality. Among the possible IQA approaches, No-Reference IQA (NR-IQA) methods are of fundamental interest, since they can be used in most real-time multimedia applications. NR-IQA are capable of assessing the quality of an image without using the reference (or pristine) image. In this paper, we investigate the use of texture descriptors in the design of NR-IQA methods. The premise is that visible impairments alter the statistics of texture descriptors, making it possible to estimate quality. To investigate if this premise is valid, we analyze the use of a set of state-of-the-art Local Binary Patterns (LBP) texture descriptors in IQA methods. Particularly, we present a comprehensive review with a detailed description of the considered methods. Additionally, we propose a framework for using texture descriptors in NR-IQA methods. Our experimental results indicate that, although not all texture descriptors are suitable for NR-IQA, many can be used with this purpose achieving a good accuracy performance with the advantage of a low computational complexity.The filters W i are learned via independent component analysis (ICA). The binarized features b i are aggregated following the same procedure described for generating the LBP labels. The descriptive features are obtained by computing the histogram of the aggregated data.Similarly to the LBP, which generates LBP channels, the BSIF generates coded images. These coded images are the set of labels generated after the binarized features are computed using Equation (17) and aggregated using Equation (1). The aggregation of BSIF results is based on a selected number of bits, instead of the number of neighbors of the labeled pixel. The labeling depends on the relationship between the patch size l and the number of binarized features b i . Figure 6 shows the BSIF coded images corresponding to the same reference using different BSIF parameters. As can be seen in this figure, the textured information depends on the patch size l and on the number of bits. The number of bits is less or equal l 2 − 1. This is the reason why the second column does not contain BSIF coded images for 9, 10, 11, or 12 bits. Figure 6 shows that the choice of the number of bits and patch sizes is important for texture analysis algorithms. Therefore, multiscale approaches that incorporate several combinations of these parameters are interesting [74][75][76][77].

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Section: Introductionmentioning

confidence: 61%

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

et al. 2018

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“…However, until now not even a single IQA metric can completely impersonate HVS for evaluation purpose. A comprehensive evaluation and survey of FR-IQA is available in [10][11][12][13]. It is still a challenging task to achieve 100% consistency with a human perception in IQA under different circumstances.…”

Section: Related Workmentioning

confidence: 99%

Full reference image quality metrics for JPEG compressed images

Gore¹,

Gupta

2015

AEU - International Journal of Electronics and Communications

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“…The average-based pooling is widely used in the literature [8,[11][12][13]. After the GMS distortion map is computed, a simple global average pooling method is applied on gradient map, as defined as follows:…”

Section: Global Average Poolingmentioning

confidence: 99%

“…However, until now not even a single IQA metric can completely impersonate HVS for evaluation purpose. A comprehensive evaluation and survey of FR-IQA is available in [8,9]. It is still a challenging task to achieve 100 % consistent with a human perception in IQA under different circumstances.…”

Section: Introductionmentioning

confidence: 98%

“…The purpose is to replace human judgment of perceived image quality with a machine evaluation. As a consequence, over the past three decades a large effort has been devoted B Akshay Gore akshaygore@live.com 1 to developing IQA measure that try to mimic human perception [1][2][3][4][5][6][7][8][9][10]. IQA techniques can be categorized as subjective versus objective.…”

Section: Introductionmentioning

confidence: 99%

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Objective color image quality assessment based on Sobel magnitude

Gupta

Gore

Kumar

et al. 2016

SIViP

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Image quality assessment algorithms aim to evaluate the perceptual quality of an image by assigning an evaluation score. By comparing the scores, the perceptual similarity or difference between two images can be assessed. In this paper, we present a new full-reference image quality metric method which was developed by combining Sobel magnitude and chrominance information in the YIQ color space. But differing from existing methods, our model incorporates color intensity adaptation to extract and enhance perceptually significant image features. The proposed metrics are tested on three well-known databases available in the literature (TID2013, TID2008, and CSIQ). Experimental results presented to confirm that the proposed metric is an effective and have low computational complexity.

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Seven challenges for image quality research

Cited by 9 publications

References 50 publications

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

On the Application LBP Texture Descriptors and Its Variants for No-Reference Image Quality Assessment

Full reference image quality metrics for JPEG compressed images

Objective color image quality assessment based on Sobel magnitude

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