scholarly journals Designing an Herbarium Digitisation Workflow with Built-In Image Quality Management

2020 ◽  
Vol 8 ◽  
Author(s):  
Abraham Nieva de la Hidalga ◽  
Paul Rosin ◽  
Xianfang Sun ◽  
Ann Bogaerts ◽  
Niko De Meeter ◽  
...  
Keyword(s):  
Image Quality ◽  
Modular Design ◽  
Image Quality Assessment ◽  
Digital Images ◽  
Quality Criteria ◽  
Audit Trail ◽  
Large Numbers ◽  
Strong Focus ◽  
Standards And Guidelines

Digitisation of natural history collections has evolved from creating databases for the recording of specimens’ catalogue and label data to include digital images of specimens. This has been driven by several important factors, such as a need to increase global accessibility to specimens and to preserve the original specimens by limiting their manual handling. The size of the collections pointed to the need of high throughput digitisation workflows. However, digital imaging of large numbers of fragile specimens is an expensive and time-consuming process that should be performed only once. To achieve this, the digital images produced need to be useful for the largest set of applications possible and have a potentially unlimited shelf life. The constraints on digitisation speed need to be balanced against the applicability and longevity of the images, which, in turn, depend directly on the quality of those images. As a result, the quality criteria that specimen images need to fulfil influence the design, implementation and execution of digitisation workflows. Different standards and guidelines for producing quality research images from specimens have been proposed; however, their actual adaptation to suit the needs of different types of specimens requires further analysis. This paper presents the digitisation workflow implemented by Meise Botanic Garden (MBG). This workflow is relevant because of its modular design, its strong focus on image quality assessment, its flexibility that allows combining in-house and outsourced digitisation, processing, preservation and publishing facilities and its capacity to evolve for integrating alternative components from different sources. The design and operation of the digitisation workflow is provided to showcase how it was derived, with particular attention to the built-in audit trail within the workflow, which ensures the scalable production of high-quality specimen images and how this audit trail ensures that new modules do not affect either the speed of imaging or the quality of the images produced.

scholarly journals Image quality assessment

2022 ◽  
pp. 4-15
Author(s):  
Y. I. Golub
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Digital Images ◽  
Image Databases ◽  
Automated Systems ◽  
Digital Formats ◽  
Proximity Measures ◽  
Visual Assessments

Quality assessment is an integral stage in the processing and analysis of digital images in various automated systems. With the increase in the number and variety of devices that allow receiving data in various digital formats, as well as the expansion of human activities in which information technology (IT) is used, the need to assess the quality of the data obtained is growing. As well as the bar grows for the requirements for their quality.The article describes the factors that deteriorate the quality of digital images, areas of application of image quality assessment functions, a method for normalizing proximity measures, classes of digital images and their possible distortions, image databases available on the Internet for conducting experiments on assessing image quality with visual assessments of experts.

scholarly journals FACTORS, CRITERIA AND REQUIREMENTS FOR FINE QUALITY OF AERIAL IMAGERY, OBTAINED FOR MAPPING PURPOSES

2020 ◽  
Vol 25 (4) ◽  
pp. 104-119
Author(s):  
Irina A. Anikeeva ◽  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Visual Inspection ◽  
Image Quality Assessment ◽  
Random Noise ◽  
Quality Criteria ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Automatic Methods

Fine image quality assessment of aerial imagery, obtained for mapping purposes, is a relevant problem today. The purpose of this article is development the criteria system of fine image quality assessment of aerial topographic imagery and set requirements to them. The article discusses a set of factors that determine the fine image quality - natural surveying conditions, its technical and technological conditions and parameters. The article carries out the analysis of how these factors influence on aerial imagery and shows the main defects of images caused by them – such as blurring, haze, loss of information in highlights and shadows, high random noise, color disbalance. The article defines the ways for identifying these defects and assessing their influence on the fine quality of aerial imagery both visual and automatic methods. It is shown that image fine quality assessment must be carried out in terms of structural and gradation (photographic) characteristics. It is also shown that, in addition to the above characteristics, fine quality of aerial images can be influenced by random factors, the appearance of which cannot be predicted. Defects caused by these factors are revealed by operator’s visual inspection. The requirements for several fine image quality criteria, which allow to establish this research phase, are given.

scholarly journals No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

2021 ◽  
Vol 7 (7) ◽  
pp. 112
Author(s):  
Domonkos Varga
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Multiple Scales ◽  
Digital Images ◽  
Input Image ◽  
Perceptual Quality ◽  
Reference Image ◽  
Benchmark Datasets ◽  
In The Wild

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).

scholarly journals A comparative analysis of non­reference assessment methods

Connectivity ◽  
2020 ◽  
Vol 148 (6) ◽  
Author(s):  
V. V. Grebenyuk ◽  
◽  
O. A. Dibrivnyy ◽  
O. V. Nehodenko
Keyword(s):  
Comparative Analysis ◽  
Image Quality ◽  
Image Quality Assessment ◽  
Excellent Result ◽  
Video Quality ◽  
Mean Value ◽  
Performance Comparison ◽  
Quality Level ◽  
Reference Methods

A comparative analysis of functions to assess image quality in the absence of a sample: no-reference (NR) measure or NR-type methods. The availability of NR-methods is very important for assessing the quality of streaming video such as television, game streaming, online conferences, web-chatting, etc. (because on the side of the recipient of the video there is no standard for quality comparison) and assessing the results of transformations aimed at improving video, and choosing the parameters of these transformations (brightness change, semitone and others). The human visual system (HVS) is able to visually assessing video quality, but If required to visually assess the quality of dozens or hundreds of videos or ranking them by quality level it will be needed a huge amount of time. Six types of experiments were performed to analyze the correlation of calculated quantitative estimates with visual assessments of the quality of the tested video files. Three of them are fundamentally new: comparing video after gamma correction and changing the contrast with different parameters, as well as blurring, which may be the result of defocusing the camcorder. A hybrid method (or reduced-reference (RR) measure) and a full-reference (FR) measure or FR-type method were also added for comparison. It has been experimentally shown that none of the studied non-reference methods of image quality assessment is universal, and the calculated assessment cannot be converted into a quality scale without taking into account the factors influencing the distortion of image quality. Moreover, all NR-type methods could not cope with the experiment of changing the contrast, believing that the best result is the most contrasting image but the original. Instead, the reference methods showed an excellent result (except one, which showed partial ineffectiveness). Also, it has been shown performance comparison between methods. It is shown that most of the studied methods calculate local estimates for each frame, and their arithmetic mean value is an estimate of the quality of the entire video file. If the video is dominated by large areas of uniform evaluation, methods of this type may give incorrect quality evaluations that do not coincide with the visual evaluations.

scholarly journals Underwater No-Reference Image Quality Assessment for Display Module of ROV

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Di Wu ◽  
Fei Yuan ◽  
En Cheng
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Reference Image ◽  
Water Medium ◽  
Display Module ◽  
Optical Images ◽  
Underwater Image ◽  
Imaging Characteristics ◽  
Monitor Image

The optical images collected by remotely operated vehicles (ROV) contain a lot of information about underwater (such as distributions of underwater creatures and minerals), which plays an important role in ocean exploration. However, due to the absorption and scattering characteristics of the water medium, some of the images suffer from serious color distortion. These distorted color images usually need to be enhanced so that we can analyze them further. However, at present, no image enhancement algorithm performs well in any scene. Therefore, in order to monitor image quality in the display module of ROV, a no-reference image quality predictor (NIPQ) is proposed in this paper. A unique property that differentiates the proposed NIPQ metric from existing works is the consideration of the viewing behavior of the human visual system and imaging characteristics of the underwater image in different water types. The experimental results based on the underwater optical image quality database (UOQ) show that the proposed metric can provide an accurate prediction for the quality of the enhanced image.

scholarly journals Interobserver variability in quality assessment of magnetic resonance images

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Rafal Obuchowicz ◽  
Mariusz Oszust ◽  
Adam Piorkowski
Keyword(s):  
Magnetic Resonance ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Interobserver Variability ◽  
Poor Performance ◽  
Magnetic Resonance Images ◽  
Perceptual Quality ◽  
Mr Images

Abstract Background The perceptual quality of magnetic resonance (MR) images influences diagnosis and may compromise the treatment. The purpose of this study was to evaluate how the image quality changes influence the interobserver variability of their assessment. Methods For the variability evaluation, a dataset containing distorted MRI images was prepared and then assessed by 31 experienced medical professionals (radiologists). Differences between observers were analyzed using the Fleiss’ kappa. However, since the kappa evaluates the agreement among radiologists taking into account aggregated decisions, a typically employed criterion of the image quality assessment (IQA) performance was used to provide a more thorough analysis. The IQA performance of radiologists was evaluated by comparing the Spearman correlation coefficients, ρ, between individual scores with the mean opinion scores (MOS) composed of the subjective opinions of the remaining professionals. Results The experiments show that there is a significant agreement among radiologists (κ=0.12; 95% confidence interval [CI]: 0.118, 0.121; P<0.001) on the quality of the assessed images. The resulted κ is strongly affected by the subjectivity of the assigned scores, separately presenting close scores. Therefore, the ρ was used to identify poor performance cases and to confirm the consistency of the majority of collected scores (ρmean = 0.5706). The results for interns (ρmean = 0.6868) supports the finding that the quality assessment of MR images can be successfully taught. Conclusions The agreement observed among radiologists from different imaging centers confirms the subjectivity of the perception of MR images. It was shown that the image content and severity of distortions affect the IQA. Furthermore, the study highlights the importance of the psychosomatic condition of the observers and their attitude.

scholarly journals DLNR-SIQA: Deep Learning-Based No-Reference Stitched Image Quality Assessment

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6457
Author(s):  
Hayat Ullah ◽  
Muhammad Irfan ◽  
Kyungjin Han ◽  
Jong Weon Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
State Of The Art ◽  
Reference Image ◽  
Primary Concern ◽  
Panoramic Images ◽  
Stitching Errors

Due to recent advancements in virtual reality (VR) and augmented reality (AR), the demand for high quality immersive contents is a primary concern for production companies and consumers. Similarly, the topical record-breaking performance of deep learning in various domains of artificial intelligence has extended the attention of researchers to contribute to different fields of computer vision. To ensure the quality of immersive media contents using these advanced deep learning technologies, several learning based Stitched Image Quality Assessment methods have been proposed with reasonable performances. However, these methods are unable to localize, segment, and extract the stitching errors in panoramic images. Further, these methods used computationally complex procedures for quality assessment of panoramic images. With these motivations, in this paper, we propose a novel three-fold Deep Learning based No-Reference Stitched Image Quality Assessment (DLNR-SIQA) approach to evaluate the quality of immersive contents. In the first fold, we fined-tuned the state-of-the-art Mask R-CNN (Regional Convolutional Neural Network) on manually annotated various stitching error-based cropped images from the two publicly available datasets. In the second fold, we segment and localize various stitching errors present in the immersive contents. Finally, based on the distorted regions present in the immersive contents, we measured the overall quality of the stitched images. Unlike existing methods that only measure the quality of the images using deep features, our proposed method can efficiently segment and localize stitching errors and estimate the image quality by investigating segmented regions. We also carried out extensive qualitative and quantitative comparison with full reference image quality assessment (FR-IQA) and no reference image quality assessment (NR-IQA) on two publicly available datasets, where the proposed system outperformed the existing state-of-the-art techniques.

Digital Imaging for Photographic Collections: Foundations for Technical Standard

2000 ◽  
Vol 29 (1) ◽  
Author(s):  
Robin Dale
Keyword(s):  
Image Quality ◽  
Digital Imaging ◽  
Digital Images ◽  
National Endowment ◽  
Technical Standard ◽  
Digital Form ◽  
Project Report

The goal of this project report, sponsored by The National Endowment for the Humanities, Division of Preservation and Access, is “to offer some guidance to libraries, archives, and museums in their efforts to convert photographic collections to digital form.” To date, there are no standards for measuring the quality of digital images created from photographs. Therefore, this report is primarily concerned with developing tools to measure image quality. Other technical and managerial issues related to digital imaging projects in general are also addressed.

scholarly journals Image Quality Assessment without Reference by Combining Deep Learning-Based Features and Viewing Distance

2021 ◽  
Vol 11 (10) ◽  
pp. 4661
Author(s):  
Aladine Chetouani ◽  
Marius Pedersen
Keyword(s):  
Image Quality ◽  
Image Quality Assessment ◽  
Feature Vector ◽  
Pearson Correlation ◽  
Image Database ◽  
Viewing Distance ◽  
Quality Metric ◽  
Image Quality Metric ◽  
Fully Connected

An abundance of objective image quality metrics have been introduced in the literature. One important essential aspect that perceived image quality is dependent on is the viewing distance from the observer to the image. We introduce in this study a novel image quality metric able to estimate the quality of a given image without reference for different viewing distances between the image and the observer. We first select relevant patches from the image using saliency information. For each patch, a feature vector is extracted from a convolutional neural network model and concatenated at the viewing distance, for which the quality is predicted. The resulting vector is fed to fully connected layers to predict subjective scores for the considered viewing distance. The proposed method was evaluated using the Colourlab Image Database: Image Quality and Viewing Distance-changed Image Database. Both databases provide subjective scores at two different viewing distances. In the Colourlab Image Database: Image Quality we obtain a Pearson correlation of 0.87 at both 50 cm and 100 cm viewing distances, while in the Viewing Distance-changed Image Database we obtained a Pearson correlation of 0.93 and 0.94 at viewing distance of four and six times the image height. The results show the efficiency of our method and its generalization ability.

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