Visual quality of color images is an important aspect in various applications of digital image processing and multimedia. A large number of visual quality metrics (indices) has been proposed recently. In order to assess their reliability, several databases of color images with various sets of distortions have been exploited. Here we present a new database called TID2013 that contains a larger number of images. Compared to its predecessor TID2008, seven new types and one more level of distortions are included. The need for considering these new types of distortions is briefly described. Besides, preliminary results of experiments with a large number of volunteers for determining the mean opinion score (MOS) are presented. Spearman and Kendall rank order correlation factors between MOS and a set of popular metrics are calculated and presented. Their analysis shows that adequateness of the existing metrics is worth improving. Special attention is to be paid to accounting for color information and observers focus of attention to locally active areas in images.

http://www.comlab.uniroma3.it/BattistiPapers/EUVIP2013_Battisti.pdf

Color image database TID2013: Peculiarities and preliminary results

Image quality assessment (IQA) has been a topic of intense research over the last several decades. With each year comes an increasing number of new IQA algorithms, extensions of existing IQA algorithms, and applications of IQA to other disciplines. In this article, I first provide an up-to-date review of research in IQA, and then I highlight several open challenges in this field. The first half of this article provides discuss key properties of visual perception, image quality databases, existing full-reference, no-reference, and reduced-reference IQA algorithms. Yet, despite the remarkable progress that has been made in IQA, many fundamental challenges remain largely unsolved. The second half of this article highlights some of these challenges. I specifically discuss challenges related to lack of complete perceptual models for: natural images, compound and suprathreshold distortions, and multiple distortions, and the interactive effects of these distortions on the images. I also discuss challenges related to IQA of images containing nontraditional, and I discuss challenges related to the computational efficiency. The goal of this article is not only to help practitioners and researchers
keep abreast of the recent advances in IQA, but to also raise awareness of the key limitations of current IQA knowledge.

/pdf/seven-challenges-in-image-quality-assessment-past-present-4y53obboox.pdf

Seven Challenges in Image Quality Assessment: Past, Present, and Future Research

Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning

Texture analysis is used in a very broad range of fields and applications, from texture classification (e.g., for remote sensing) to segmentation (e.g., in biomedical imaging), passing through image synthesis or pattern recognition (e.g., for image inpainting). For each of these image processing procedures, first, it is necessary to extract—from raw images—meaningful features that describe the texture properties. Various feature extraction methods have been proposed in the last decades. Each of them has its advantages and limitations: performances of some of them are not modified by translation, rotation, affine, and perspective transform; others have a low computational complexity; others, again, are easy to implement; and so on. This paper provides a comprehensive survey of the texture feature extraction methods. The latter are categorized into seven classes: statistical approaches, structural approaches, transform-based approaches, model-based approaches, graph-based approaches, learning-based approaches, and entropy-based approaches. For each method in these seven classes, we present the concept, the advantages, and the drawbacks and give examples of application. This survey allows us to identify two classes of methods that, particularly, deserve attention in the future, as their performances seem interesting, but their thorough study is not performed yet.

/pdf/texture-feature-extraction-methods-a-survey-4pjtaq24uv.pdf

Texture Feature Extraction Methods: A Survey

Finding ways to monitor and control the perceptual quality of digital visual media has become a pressing concern as the volume being transported and viewed continues to increase exponentially. This paper discusses the principles and methods of modern algorithms for automatically predicting the quality of visual signals. By casting the problem as analogous to assessing the efficacy of a visual communication system, it is possible to divide the quality assessment problem into understandable modeling subproblems. Along the way, we will visit models of natural images and videos, of visual perception, and a broad spectrum of applications.

/pdf/automatic-prediction-of-perceptual-image-and-video-quality-1276xl9y2c.pdf

Automatic Prediction of Perceptual Image and Video Quality

In the paper a new combined image quality metric is proposed, which is based on three methods previously described by various researchers. The main advantage of the presented approach is the strong linear correlation with the subjective scores without additional nonlinear mapping. The values and the obtained correlation coefficients of the proposed metric have been compared with some other state-of-art ones using two largest publicly available image databases including the subjective quality scores.

Combined full-reference image quality metric linearly correlated with subjective assessment

Automatic text recognition from the natural images acquired in uncontrolled lighting conditions is a challenging task due to the presence of shadows hindering the shape analysis and classification of individual characters. Since the optical character recognition methods require prior image binarization, the application of classical global thresholding methods in such case makes it impossible to preserve the visibility of all characters. Nevertheless, the use of adaptive binarization does not always lead to satisfactory results for heavily unevenly illuminated document images. In this paper, the image preprocessing methodology with the use of local image entropy filtering is proposed, allowing for the improvement of various commonly used image thresholding methods, which can be useful also for text recognition purposes. The proposed approach was verified using a dataset of 140 differently illuminated document images subjected to further text recognition. Experimental results, expressed as Levenshtein distances and F-Measure values for obtained text strings, are promising and confirm the usefulness of the proposed approach.

Improvement of Image Binarization Methods Using Image Preprocessing with Local Entropy Filtering for Alphanumerical Character Recognition Purposes.

In the paper the idea of the combined image quality metric based on the structural and feature similarity comparison is discussed. Since most of image quality assessment methods developed during last years require the nonlinear mapping to obtain high correlation with subjective quality scores, there is an important problem of choosing the proper mapping function and its optimal parameters in practical applications. The most common approach is the utilization of the logistic function but its parameters strongly depend on the specific image quality database. To avoid the necessity of such mapping the nonlinear combination of three known image quality metrics is proposed and verified for seven currently available image quality assessment databases in terms of the linear correlation with subjective scores.

Combined image similarity index

A rapid development and growing popularity of additive manufacturing technology leads to new challenging tasks allowing not only a reliable monitoring of the progress of the 3D printing process but also the quality of the printed objects. The automatic objective assessment of the surface quality of the 3D printed objects proposed in the paper, which is based on the analysis of depth maps, allows for determining the quality of surfaces during printing for the devices equipped with the built-in 3D scanners. In the case of detected low quality, some corrections can be made or the printing process may be aborted to save the filament, time and energy. The application of the entropy analysis of the 3D scans allows evaluating the surface regularity independently on the color of the filament in contrast to many other possible methods based on the analysis of visible light images. The results obtained using the proposed approach are encouraging and further combination of the proposed approach with camera-based methods might be possible as well.

https://www.mdpi.com/1099-4300/21/1/97/pdf

Objective 3D Printed Surface Quality Assessment Based on Entropy of Depth Maps.

In the paper the analysis of the influence of the colour space on the results obtained during image quality assessment using the Structural Similarity index and the Singular Value Decomposition approach has been investigated. Obtained results have been compared to the ones achieved by widely used Normalised Colour Difference (NCD) metric. All the calculations have been performed using the LIVE Image Quality Assessment Database in order to compare the correlation of achieved results with the Differential Mean Opinion Score (DMOS) values obtained from the LIVE database. As a good solution for the further research, also with the use of some other image quality metrics, the application of the HSV colour space is proposed instead of commonly used YUV/YIQ luminance channel or the average of the RGB channels.

Krzysztof Okarma

Papers

Combined full-reference image quality metric linearly correlated with subjective assessment

Improvement of Image Binarization Methods Using Image Preprocessing with Local Entropy Filtering for Alphanumerical Character Recognition Purposes.

Combined image similarity index

Objective 3D Printed Surface Quality Assessment Based on Entropy of Depth Maps.

Colour Image Quality Assessment Using Structural Similarity Index and Singular Value Decomposition