An Ingenious Application-Specific Quality Assessment Methods for Compressed Wireless Capsule Endoscopy Images

<p>Image quality assessment methods are used in different image processing applications. Among them, image compression and image super-resolution can be mentioned in wireless capsule endoscopy (WCE) applications. The existing image compression algorithms for WCE employ the generalpurpose image quality assessment (IQA) methods to evaluate the quality of the compressed image. Due to the specific nature of the images captured by WCE, the general-purpose IQA methods are not optimal and give less correlated results to that of subjective IQA (visual perception). This paper presents improved image quality assessment techniques for wireless capsule endoscopy applications. The proposed objective IQA methods are obtained by modifying the existing full-reference image quality assessment techniques. The modification is done by excluding the noninformative regions, in endoscopic images, in the computation of IQA metrics. The experimental results demonstrate that the proposed IQA method gives an improved peak signal-tonoise ratio (PSNR) and structural similarity index (SSIM). The proposed image quality assessment methods are more reliable for compressed endoscopic capsule images.</p>

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Comparison of modern nonlinear multichannel filtering techniques using recent full-reference image quality assessment methods

Annales Universitatis Mariae Curie-Sklodowska sectio AI – Informatica ◽

10.2478/v10065-008-0009-1 ◽

2008 ◽

Vol 8 (1) ◽

Author(s):

Krzysztof Okarma

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Assessment Methods ◽

Reference Image ◽

Full Reference ◽

Multichannel Filtering ◽

Filtering Techniques

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NO-REFERENCE IMAGE QUALITY MEASURE FOR IMAGES WITH MULTIPLE DISTORTIONS USING RANDOM FORESTS FOR MULTI METHOD FUSION

Image Analysis & Stereology ◽

10.5566/ias.1534 ◽

2018 ◽

Vol 37 (2) ◽

pp. 105 ◽

Cited By ~ 1

Author(s):

Kanjar De ◽

Masilamani V

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Reference Image ◽

Multiple Sources ◽

Noise Image ◽

Assessment Techniques ◽

Image Quality Measure ◽

Channel Errors

Over the years image quality assessment is one of the active area of research in image processing. Distortion in images can be caused by various sources like noise, blur, transmission channel errors, compression artifacts etc. Image distortions can occur during the image acquisition process (blur/noise), image compression (ringing and blocking artifacts) or during the transmission process. A single image can be distorted by multiple sources and assessing quality of such images is an extremely challenging task. The human visual system can easily identify image quality in such cases, but for a computer algorithm performing the task of quality assessment is a very difficult. In this paper, we propose a new no-reference image quality assessment for images corrupted by more than one type of distortions. The proposed technique is compared with the best-known framework for image quality assessment for multiply distorted images and standard state of the art Full reference and No-reference image quality assessment techniques available.

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Combined Full-Reference Image Quality Metrics for Objective Assessment of Multiply Distorted Images

Electronics ◽

10.3390/electronics10182256 ◽

2021 ◽

Vol 10 (18) ◽

pp. 2256

Author(s):

Krzysztof Okarma ◽

Piotr Lech ◽

Vladimir V. Lukin

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Objective Assessment ◽

Assessment Methods ◽

Subjective Quality ◽

Reference Image ◽

Exponential Weights ◽

Full Reference ◽

Highly Correlated

In the recent years, many objective image quality assessment methods have been proposed by different researchers, leading to a significant increase in their correlation with subjective quality evaluations. Although many recently proposed image quality assessment methods, particularly full-reference metrics, are in some cases highly correlated with the perception of individual distortions, there is still a need for their verification and adjustment for the case when images are affected by multiple distortions. Since one of the possible approaches is the application of combined metrics, their analysis and optimization are discussed in this paper. Two approaches to metrics’ combination have been analyzed that are based on the weighted product and the proposed weighted sum with additional exponential weights. The validation of the proposed approach, carried out using four currently available image datasets, containing multiply distorted images together with the gathered subjective quality scores, indicates a meaningful increase of correlations of the optimized combined metrics with subjective opinions for all datasets.

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Adaptation of Full-Reference Image Quality Assessment Methods for Automatic Visual Evaluation of the Surface Quality of 3D Prints

Elektronika ir Elektrotechnika ◽

10.5755/j01.eie.25.5.24357 ◽

2019 ◽

Vol 25 (5) ◽

pp. 57-62 ◽

Cited By ~ 4

Author(s):

Krzysztof Okarma ◽

Jaroslaw Fastowicz

Keyword(s):

Additive Manufacturing ◽

Image Quality ◽

Quality Assessment ◽

Surface Quality ◽

Quality Evaluation ◽

Image Quality Assessment ◽

Assessment Methods ◽

Reference Image ◽

Visual Evaluation ◽

Full Reference

Automatic visual quality assessment of the 3D printed surfaces is currently one of the most demanding challenges in additive manufacturing. Regardless of the applications of the computer vision for the 3D printing process monitoring purposes, a reliable surface quality evaluation during manufacturing may introduce brand new possibilities. The detection of some distortions and their automatic evaluation can be helpful when deciding to stop the process to save time, energy, and filament. In some cases, some further corrections can also be made for relatively small distortions. Since many general-purpose image quality assessment methods have been proposed in recent years, their applications for the quality evaluation in the additive manufacturing are investigated. As most of the metrics are full-reference and require the availability of the original perfect quality image, their direct application is not possible. Therefore, their adaptation is described in the paper together with experimental verification of classification results obtained using various metrics.

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No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics

Journal of Imaging Science and Technology ◽

10.2352/j.imagingsci.technol.2020.64.1.010505 ◽

2020 ◽

Vol 64 (1) ◽

pp. 10505-1-10505-16

Author(s):

Yin Zhang ◽

Xuehan Bai ◽

Junhua Yan ◽

Yongqi Xiao ◽

C. R. Chatwin ◽

...

Keyword(s):

Neural Network ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Assessment Method ◽

Image Distortion ◽

Image Feature ◽

Reference Image ◽

Gradient Magnitude ◽

Distortion Type

Abstract A new blind image quality assessment method called No-Reference Image Quality Assessment Based on Multi-Order Gradients Statistics is proposed, which is aimed at solving the problem that the existing no-reference image quality assessment methods cannot determine the type of image distortion and that the quality evaluation has poor robustness for different types of distortion. In this article, an 18-dimensional image feature vector is constructed from gradient magnitude features, relative gradient orientation features, and relative gradient magnitude features over two scales and three orders on the basis of the relationship between multi-order gradient statistics and the type and degree of image distortion. The feature matrix and distortion types of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion type; the feature matrix and subjective scores of known distorted images are used to train an AdaBoost_BP neural network to determine the image distortion degree. A series of comparative experiments were carried out using Laboratory of Image and Video Engineering (LIVE), LIVE Multiply Distorted Image Quality, Tampere Image, and Optics Remote Sensing Image databases. Experimental results show that the proposed method has high distortion type judgment accuracy and that the quality score shows good subjective consistency and robustness for all types of distortion. The performance of the proposed method is not constricted to a particular database, and the proposed method has high operational efficiency.

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