Edge-texture 2D image quality metrics suitable for evaluation of image interpolation algorithms

Maksimović-Moićević, Sanja; Lukač, Željko; Temerinac, Miodrag

doi:10.2298/csis140402003m

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Zujovic [46] develop another metric for texture similarity that account for human visual perception and the stochastic nature of textures. However and in another work, a proposed method (TIQM) consists of a quality measure which qualify images in texture areas [47]. As a document image quality assessment, Alaei [48] presents an IQA based on texture similarity index (LBPSI) obtained by local LBP features.…”

Section: Textural Image Quality Evaluation 31 Image Quality Assessmentmentioning

confidence: 99%

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Bouida

Beladgham

Bassou

et al. 2020

IJEECS

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<span>In biometric systems, compression takes important place especially in order to reduce the size of the information stored or transmitted through the distributed biometric systems. It is also noted that the compression techniques induce loss of information in the compressed images that can affect the effectiveness of biometric systems. The main objective of our contribution is to examine the efficacy of the used method to offer an optimal compression quality in these kind of images without considerable distortion. In order to evaluate the efficacy of the compression process, we use two kinds of evaluation, full-reference image quality assessment and a new proposed textural quality analysis of the compressed images. In this paper, we use a second-generation wavelet transform to improve the compression study in biometric images. The basic idea of this algorithm is the quincunx wavelet transform coupled to a modified progressive encoder called SPIHT-Z encoding.</span>

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Section: Textural Image Quality Evaluation 31 Image Quality Assessmentmentioning

confidence: 99%

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Bouida

Beladgham

Bassou

et al. 2020

IJEECS

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“…Two‐dimensional image quality metrics [15] contains two image quality indices, in edge areas of an image (eIQM) and in texture areas of this image (tIQM). The first step is separation of image pixels in edge and texture areas.…”

Section: Subjective and Objective Image Quality Image Quality Assesmentioning

confidence: 99%

“…Following perception sensibility on edges in an image, ‘video quality measure‘(VQM) has been proposed [14] and even included in the ITU recommendations. Finally ‘2D image quality mMeasure‘(2D IQM) has been proposed in [15] estimating image quality in edge and texture areas. This measure also provides a quantified image content indicator – percent of pixels in edge areas of an image.…”

Section: Introductionmentioning

confidence: 99%

Objective estimation of subjective image quality assessment using multi‐parameter prediction

2019

Self Cite

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Objective evaluation of a subjective image quality assessment plays a significant role in the various image processing applications, such as compression, interpolation and noise reduction. The subjective image quality assessment does not only depend on some objective measurable artefacts, but also on image content and kind of distortions. Thus, a multi‐parameter prediction of the objective image quality assessment is proposed in this study. The prediction parameters are found minimising the mean square error related to the known subjective image quality measure (DMOS). This approach includes mostly used image quality metrics (peak signal‐to‐noise ratio, multi‐scale structural similarity image measure, feature similarity image measure, video quality measure) and two‐dimensional image quality metrics (2D IQM). The proposed multi‐parameter prediction has been verified on the test image database (LIVE) for compression, noise and blur distortions with available subjective image quality measures (DMOS). More reliable estimations are obtained using multi‐parameter prediction instead of only one measure. The best results are reached when an image content indicator is combined with the 2D IQM measure separately for different kinds of distortions.

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“…To this end, various IQA methods have been proposed. According to the usage of the reference image, objective IQA metrics can be divided into three categories: full reference (FR) [4,5], reduced-reference (RR) [6], and blind/no-reference (NR) [7]. amplitudes and different frequency signals.…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment on Gaussian Blur Images

Wang¹,

Wang²,

Zhou³

2017

J Inf Process Syst

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Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

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Edge-texture 2D image quality metrics suitable for evaluation of image interpolation algorithms

Cited by 4 publications

References 20 publications

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Quality and texture analysis of biometric images compressed with second-generation wavelet transforms and SPIHT-Z encoder

Objective estimation of subjective image quality assessment using multi‐parameter prediction

Blind Image Quality Assessment on Gaussian Blur Images

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