The essential guide to the entire process behind performing a complete characterization and benchmarking of cameras through image quality analysis Camera Image Quality Benchmarking contains the basic information and approaches for the use of subjectively correlated image quality metrics and outlines a framework for camera benchmarking. The authors show how to quantitatively compare image quality of cameras used for consumer photography. This book helps to fill a void in the literature by detailing the types of objective and subjective metrics that are fundamental to benchmarking still and video imaging devices. Specifically, the book provides an explanation of individual image quality attributes and how they manifest themselves to camera components and explores the key photographic still and video image quality metrics. The text also includes illustrative examples of benchmarking methods so that the practitioner can design a methodology appropriate to the photographic usage in consideration. The authors outline the various techniques used to correlate the measurement results from the objective methods with subjective results. The text also contains a detailed description on how to set up an image quality characterization lab, with examples where the methodological benchmarking approach described has been implemented successfully. This vital resource: Explains in detail the entire process behind performing a complete characterization and benchmarking of cameras through image quality analysis Provides best practice measurement protocols and methodologies, so readers can develop and define their own camera benchmarking system to industry standards Includes many photographic images and diagrammatical illustrations to clearly convey image quality concepts Champions benchmarking approaches that value the importance of perceptually correlated image quality metrics Written for image scientists, engineers, or managers involved in image quality and evaluating camera performance, Camera Image Quality Benchmarking combines knowledge from many different engineering fields, correlating objective (perception-independent) image quality with subjective (perception-dependent) image quality metrics.

This tutorial provides a means to estimate resolution requirements for the use of digital imaging technology in converting text-based material. It is suggested that benchmarks for resolution can be calculated by evaluating the physical attributes of source documents and by applying Quality Index formulas that have been derived from those established for preservation microfilming. The formulas are based on three variables: the height of the smallest significant character, the desired quality to be obtained in the reformatted version, and the resolution of the recording device. For the past several years, the Cornell University Department of Preservation and Conservation (New York) has experimented using the Digital Quality Index formula to predict resolution requirements for a wide range of documents using a number of scanning devices. The results of these and other experiments confirm the utility of the Digital Quality Index formula for bitonal (black and white) scanning, but a slight modification to the formula is recommended in cases where grayscale scanning is employed. It is cautioned that the use of these formulas are for benchmarking purposes only. Assessment methods for scanner performance are discussed, and a continuous quality assurance program is recommended. Sections include: "Where to Begin"; "How To Determine Digital Image Quality"; "Scanning Methodologies and Compression Techniques"; "Bitonal Scanning"; "Grayscale Scanning"; "Verifying the Predicted Quality Index"; "Visual Inspection"; "Suggested Guidelines" and "Summary of Recommendations." Seven figures and three tables illustrate the process and data. (Contains 20 references.) (MAS)

Understanding plant roots and root development is key for agricultural productivity as roots affect many plant, ecosystem and biogeochemical processes. However, studying root development in-situ can be challenging. Minirhizotrons are commonly used for root monitoring, and allow non-destructive tracking of root development overtime. Nonetheless, existing commercial minirhizotron cameras are expensive and manually operated. Planar optodes are a promising technology for quantifying concentrations of soil solutes, but have not yet been paired with minirhizotron technology. We developed an inexpensive, automated minirhizotron camera system, the RhizoPi camera, built using off-the-shelf computer components that can be paired with planar optodes. The purpose of this study was to evaluate the capability and utility of the RhizoPi camera system. The objectives were to 1) assess the capabilities and design of the RhizoPi minirhizotron camera system and benchmark it against a commercial minirhizotron camera; 2) create an image analysis script to analyze minirhizotron images and compare results to those of existing commercial software; and 3) develop a method for using the RhizoPi camera system for planar optode imaging. Objectives 1 and 2 are the focus of Chapter 2 in which soybean (Glycine max L.) was grown in containers under controlled greenhouse conditions. Images collected using the RhizoPi camera system were analyzed for percentage of images that are roots (root percentage) using a script written in the Python programming language and the RootSnap!® software. Although the average root percentage measured by the Python script, 3.36%, was significantly larger than with RootSnap!, 2.97%, the difference was small in magnitude (0.39%). Images collected using the RhizoPi camera were compared to images collected on the same day using the commercially-available CID Bioscience CI-600 minirhizotron camera. Images from the two camera systems were processed to ensure the exact image frame location was being compared, then processed using RootSnap!. Objective 3 was the focus of Chapter 3, in which a method is presented for augmenting the RhizoPi minirhizotron camera system with planar oxygen optode technology and techniques. In this method acrylic minirhizotron tubes were turned into oxygen sensitive planar optodes by applying oxygen-sensitive dyes in a strip along the length of each tube. Successful calibrations of these optodes are presented as a proof of concept for this method. The RhizoPi camera system is capable of collecting research-quality images of roots, and can serve as a platform for deploying planar optode technologies for in situ analysis of soil solution chemistry.

This Lecture book is about objective image quality assessment—where the aim is to provide computational models that can automatically predict perceptual image quality. The early years of the 21st century have witnessed a tremendous growth in the use of digital images as a means for representing and communicating information. A considerable percentage of this literature is devoted to methods for improving the appearance of images, or for maintaining the appearance of images that are processed. Nevertheless, the quality of digital images, processed or otherwise, is rarely perfect. Images are subject to distortions during acquisition, compression, transmission, processing, and reproduction. To maintain, control, and enhance the quality of images, it is important for image acquisition, management, communication, and processing systems to be able to identify and quantify image quality degradations. The goals of this book are as follows; a) to introduce the fundamentals of image quality assessment, and to explain the relevant engineering problems, b) to give a broad treatment of the current state-of-the-art in image quality assessment, by describing leading algorithms that address these engineering problems, and c) to provide new directions for future research, by introducing recent models and paradigms that significantly differ from those used in the past. The book is written to be accessible to university students curious about the state-of-the-art of image quality assessment, expert industrial R&D engineers seeking to implement image/video quality assessment systems for specific applications, and academic theorists interested in developing new algorithms for image quality assessment or using existing algorithms to design or optimize other image processing applications.