Distance-Entropy: An Effective Indicator for Selecting Informative Data

Smart agriculture is inseparable from data gathering, analysis, and utilization. A high-quality data improves the efficiency of intelligent algorithms and helps reduce the costs of data collection and transmission. However, the current image quality assessment research focuses on visual quality, while ignoring the crucial information aspect. In this work, taking the crop pest recognition task as an example, we proposed an effective indicator of distance-entropy to distinguish the good and bad data from the perspective of information. Many comparative experiments, considering the mapping feature dimensions and base data sizes, were conducted to testify the validity and robustness of this indicator. Both the numerical and the visual results demonstrate the effectiveness and stability of the proposed distance-entropy method. In general, this study is a relatively cutting-edge work in smart agriculture, which calls for attention to the quality assessment of the data information and provides some inspiration for the subsequent research on data mining, as well as for the dataset optimization for practical applications.

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Face Image Quality Assessment: A Literature Survey

ACM Computing Surveys ◽

10.1145/3507901 ◽

2022 ◽

Author(s):

Torsten Schlett ◽

Christian Rathgeb ◽

Olaf Henniger ◽

Javier Galbally ◽

Julian Fierrez ◽

...

Keyword(s):

Deep Learning ◽

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Face Image ◽

Quality Data ◽

Learning Approaches ◽

The Face ◽

Future Work

The performance of face analysis and recognition systems depends on the quality of the acquired face data, which is influenced by numerous factors. Automatically assessing the quality of face data in terms of biometric utility can thus be useful to detect low-quality data and make decisions accordingly. This survey provides an overview of the face image quality assessment literature, which predominantly focuses on visible wavelength face image input. A trend towards deep learning based methods is observed, including notable conceptual differences among the recent approaches, such as the integration of quality assessment into face recognition models. Besides image selection, face image quality assessment can also be used in a variety of other application scenarios, which are discussed herein. Open issues and challenges are pointed out, i.a. highlighting the importance of comparability for algorithm evaluations, and the challenge for future work to create deep learning approaches that are interpretable in addition to providing accurate utility predictions.

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A Novel Hybrid Image Quality Assessment Algorithm

Indian Journal Of Applied Research ◽

10.15373/2249555x/apr2014/31 ◽

2011 ◽

Vol 4 (4) ◽

pp. 107-108

Author(s):

Deepa Maria Thomas ◽

◽

S. John Livingston

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment

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Implementation of No Reference Distortion Patch Features Image Quality Assessment Algorithm Based on Human Visual System

International Journal on Communications Antenna and Propagation (IRECAP) ◽

10.15866/irecap.v4i5.4166 ◽

2014 ◽

Vol 4 (5) ◽

pp. 195

Author(s):

Ch. Subrahmanyam ◽

D. Venkata Rao ◽

N. Usha Rani

Keyword(s):

Image Quality ◽

Visual System ◽

Quality Assessment ◽

Human Visual System ◽

Image Quality Assessment

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Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.9.iqsp-323 ◽

2020 ◽

Vol 2020 (9) ◽

pp. 323-1-323-8

Author(s):

Litao Hu ◽

Zhenhua Hu ◽

Peter Bauer ◽

Todd J. Harris ◽

Jan P. Allebach

Keyword(s):

Image Quality ◽

Quality Assessment ◽

Image Quality Assessment ◽

Research Area ◽

Input Image ◽

Quality Score ◽

Document Image ◽

Digital Cameras ◽

Active Research ◽

Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

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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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