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2018 | Buch

Introduction Kapitel lesen Erstes Kapitel lesen

Visual Quality Assessment for Natural and Medical Image

verfasst von: Yong Ding

Verlag: Springer Berlin Heidelberg

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Image quality assessment (IQA) is an essential technique in the design of modern, large-scale image and video processing systems. This book introduces and discusses in detail topics related to IQA, including the basic principles of subjective and objective experiments, biological evidence for image quality perception, and recent research developments. In line with recent trends in imaging techniques and to explain the application-specific utilization, it particularly focuses on IQA for stereoscopic (3D) images and medical images, rather than on planar (2D) natural images. In addition, a wealth of vivid, specific figures and formulas help readers deepen their understanding of fundamental and new applications for image quality assessment technology.

This book is suitable for researchers, clinicians and engineers as well as students working in related disciplines, including imaging, displaying, image processing, and storage and transmission. By reviewing and presenting the latest advances, and new trends and challenges in the field, it benefits researchers and industrial R&D engineers seeking to implement image quality assessment systems for specific applications or design/optimize image/video processing algorithms.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Introduction
Abstract
Image quality assessment (IQA) is an essential technique in the design of modern image and video processing systems. With the increasing demand in high-quality images for daily lives, industry, academic, etc., the significance of IQA study is highlighted. Correspondingly, in the last few decades, great progress has been witnessed. Nowadays, both subjective and objective IQA researches have become mature and systematic research topics. In addition, the applications of IQA are extended to novel and specific scenarios. This chapter explains the importance of IQA in detail and concludes the organization of this book. As will be introduced, the discussions about subjective and objective IQA, as well as the extended and specific application scenarios including stereoscopic/3D and medical IQA, are all contained in this book.
Yong Ding
Chapter 2. Subjective Ratings and Image Quality Databases
Abstract
Since human visual system (HVS) is the ultimate receiver of visual signals, ideal image quality assessment (IQA) should be conducted by subjective experiments. Moreover, well-organized subjective study provides the golden standard for evaluating and training objective IQA models. A few IQA databases have been constructed following rigorous experimental setups and flows. Such databases provide subjective experimental results for collected distorted images that can well guide objective study. In this chapter, the general framework of subjective IQA study and the information about several representative IQA databases are introduced.
Yong Ding
Chapter 3. Human Visual System and Vision Modeling
Abstract
The computational modeling of human visual system (HVS) is closely connected with image quality assessment (IQA) since visual signal quality is always finally evaluated by the former. Therefore, basic knowledge about HVS, especially its parts that are in charge of quality perception, should be aware of for studying IQA. This chapter gives a general introduction to the anatomy structure and the important properties of HVS. The anatomy structure gives a straightforward understanding upon HVS, including the hierarchical signal transmitting and processing flow and the responsibilities of each specific part. The properties of HVS are abstraction of this biological basis that is concluded to offer potential instructions for the design of objective IQA methods.
Yong Ding
Chapter 4. General Framework of Image Quality Assessment
Abstract
The study upon objective image quality assessment (IQA) has entered the era of solid theoretical fundaments and rigorous experimental flows. Although there are extensive IQA methods been proposed, they generally follow similar frameworks in design. The main differences between frameworks are according to how much the implement of a specific method is dependent upon reference images, in which way three classes, full-reference (FR), reduced-reference (RR), and no-reference (NR), are defined. For methods of all the three categories, evitable processing contains quality-aware feature extraction, feature quantification, quality index mapping, and statistical performance evaluation, and the related fields include image processing, statistics, machine learning at the very least. In this chapter, we attempt to introduce the general frameworks that modern IQA methods adopt, explain the specific flow of the methods step-by-step, during which major knowledge about the design and evaluation of the methods would be concerned.
Yong Ding
Chapter 5. Image Quality Assessment Based on Human Visual System Properties
Abstract
Utilizing the properties of human visual system (HVS) is a major source of inspirations for the design of image quality assessment (IQA) methods. With the current research status of neuroscience and human vision perception, although a rigorous simulation of HVS is still far from possible, novel ideas can be enlightened. The basic structures of HVS have been previously discussed in Chap. 3, and the goal of this chapter is to connect IQA design with certain knowledge about HVS that can be made use of. More specifically, because we have now been aware that the operation of HVS is actually under a hierarchical structure, it is feasible to study the characteristics of its individual processing stages; on the other hand, if the inner structures are neglected and the HVS is regarded as a Black box, studying its external responses is another potential for providing solutions. This chapter will provide introduction to methods employing these strategies.
Yong Ding
Chapter 6. Image Quality Assessment Based on Natural Image Statistics
Abstract
Since human visual system (HVS) is highly adapted to extract statistical information from the viewing scenes, extracting and mathematically modeling natural scene statistics (NSS) is a promising solution for image quality assessment (IQA), as an alteration for simulating HVS properties that is discussed in the previous chapter. Depending on how statistics information is modeled, in this chapter, we conclude and introduce several representative NSS-based types of methods. The first class of methods discussed in the chapter are based on the hypothesis underlying structural similarity, which assume the natural images are highly structured, and lower-quality images fail to have the similar structural information. Then, methods with local textural information extraction aiming at utilizing the statistical distribution changing with distort to measure distortion are introduced. Subsequently, the methods based on finding hidden independent components in nature images are presented. Finally, we put forward the methods that extract quality-aware features based on multifractal analysis, which capture the statistical complexity information of images in accordance with HVS. It is really worthy to point out that exploiting the image information jointly in different domains is necessary and constructive.
Yong Ding
Chapter 7. Stereoscopic Image Quality Assessment
Abstract
One of the major trends of image quality assessment (IQA) is to develop from the 2D (planar) natural images to 3D (stereoscopic) ones, mainly because of the developing technique of 3D acquisition and display equipment. The necessity of studying stereoscopic IQA (IQA) is due to that the findings of 2D IQA are not feasible to be directly implemented. Although a stereoscopic image is merely consisted of an image pair, the binocular effects it brings about made SIQA a much more mysterious puzzle than directly averaging the results of assessing two planar images. Comparing to its 2D counterpart, SIQA research is undoubtedly still at an early stage. Fortunately, SIQA has received lots of attention in the most recent years, and significant progresses have been witnessed. Similar to 2D IQA, the theories of SIQA are built based upon the biological grounds of human visual system, especially its properties related to binocular vision. Therefore, the discussions in this chapter will start from basic concepts about stereoscopic images and binocular vision, which is followed by introduction to subjective and objective SIQA researches.
Yong Ding
Chapter 8. Medical Image Quality Assessment
Abstract
Medical image quality assessment (MIQA) is of great significance to the development of medical imaging technology, which is widely used in computer-aided detection and diagnosis of diseases. However, MIQA evaluates the quality of images according to how well they offer useful and effective presentation to assist with physicians in diagnosing, which is greatly different from the purposes of natural image quality assessment. In this chapter, we present some of the new advances in MIQA by taking some application tasks for instances. The first case concerns evaluating the quality of portable fundus camera photographs, which is used with telemedicine and plays an important role in ophthalmology. The next example is the study on a more advanced type of imaging techniques, which is called susceptibility weighted imaging. The followed case is an adaptive paralleled sinogram noise reduction method based on relative quality assessment provided, which can increase both efficiency and performance of low-dose computed tomography (CT) noise reduction algorithms. The lastly presented study concentrates on the relationship between the image quality and imaging dose in low-dose cone beam CT.
Yong Ding
Chapter 9. Challenge Issues and Future Work
Abstract
The past two decades have witnessed significant progresses of image quality assessment (IQA) and led it to a comparatively mature stage, yet there are still many challenges that researchers are yet to overcome. On one hand, there are problems that have always been troubling and demanding attention, e.g., the principle of subjective study, how to find image features that are quality-aware, etc. On the other hand, the attention of researchers has been shifted to some novel problems that are not regarded as serious before, e.g., the application scenarios, the computational efficiency. In this chapter, we try our best to list the new and old issues that are deemed most challenging, as well as give some expectations for the nearer and further future.
Yong Ding
Backmatter
Metadaten
Titel
Visual Quality Assessment for Natural and Medical Image
verfasst von
Yong Ding
Copyright-Jahr
2018
Verlag
Springer Berlin Heidelberg
Electronic ISBN
978-3-662-56497-4
Print ISBN
978-3-662-56495-0
DOI
https://doi.org/10.1007/978-3-662-56497-4

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