Architects Journal Metric Handbook By Leslie Fairweather Full PDF

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The 13-digit and 10-digit formats both work. Please try again.Please try again.Please try again. Used: Very GoodPlease choose a different delivery location or purchase from another seller.But harnessing its potential requires knowledge of color science, systems, processing algorithms, and device characteristics-topics drawn from a broad range of disciplines. One can acquire the requisite background with an armload of physics, chemistry, engineering, computer science, and mathematics books and journals- or one can find it here, in the Digital Color Imaging Handbook. Unprecedented in scope, this handbook presents, in a single concise and authoritative publication, the elements of these diverse areas relevant to digital color imaging. The first three chapters cover the basics of color vision, perception, and physics that underpin digital color imaging. The remainder of the text presents the technology of color imaging with chapters on color management, device color characterization, digital halftoning, image compression, color quantization, gamut mapping, computationally efficient transform algorithms, and color image processing for digital cameras. Each chapter is written by world-class experts and largely self-contained, but cross references between chapters reflect the topics' important interrelations. Supplemental materials are available for download from the CRC Web site, including electronic versions of some of the images presented in the book. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required. Full content visible, double tap to read brief content. Videos Help others learn more about this product by uploading a video. Upload video To calculate the overall star rating and percentage breakdown by star, we don’t use a simple average. Instead, our system considers things like how recent a review is and if the reviewer bought the item on Amazon. It also analyzes reviews to verify trustworthiness.

architects journal metric handbook by leslie fairweather.

Please try again later. Prof. Yuzo Iano 5.0 out of 5 stars. The 13-digit and 10-digit formats both work. Please try again.Please try again.Please try again. But harnessing its potential requires knowledge of color science, systems, processing algorithms, and device characteristics-topics drawn from a broad range of disciplines. December 23, 2002CRC PressDecember 21, 2017CRC PressWhere the content of the eBook requires a specific layout, or contains maths or other special characters, the eBook will be available in PDF (PBK) format, which cannot be reflowed. For both formats the functionality available will depend on how you access the ebook (via Bookshelf Online in your browser or via the Bookshelf app on your PC or mobile device). But harnessing its potential requires knowledge of color science, systems, processing algorithms, and device characteristics-topics drawn from a broad range of disciplines. Supplemental materials are available for download from the CRC Web site, including electronic versions of some of the images presented in the book.To learn how to manage your cookie settings, please see our. But harnessing its potential requires knowledge of color science, systems, processing algorithms, and device characteristics-topics drawn from a broad range of disciplines. Supplemental materials are available for download from the CRC Web site, including electronic versions of some of the images presented in the book. Condition: New. New Book. Shipped from UK. Established seller since 2000.Condition: New. New Book. Shipped from UK. Established seller since 2000.All Rights Reserved. New Delhi: CRC, December 23, 2002;CRC Press LLC, 2002. Hardcover. Very Good. May have limited writing in cover pages.CRC Press, 2002-12-23. Hardcover. Good.Used - Good.Millions of books are added to our site everyday and when we find one that matches your search, we'll send you an e-mail. Best of all, it's free. Read the rules here. Some features of WorldCat will not be available.

By continuing to use the site, you are agreeing to OCLC’s placement of cookies on your device. Find out more here. However, formatting rules can vary widely between applications and fields of interest or study. The specific requirements or preferences of your reviewing publisher, classroom teacher, institution or organization should be applied. Please enter recipient e-mail address(es). Please re-enter recipient e-mail address(es). Please enter your name. Please enter the subject. Please enter the message. Author: Gaurav SharmaThis book covers the basics of color vision, perception, and physics that underpin the subject. It presents the technology of color imaging. Please select Ok if you would like to proceed with this request anyway. All rights reserved. You can easily create a free account. You can remove the unavailable item(s) now or we'll automatically remove it at Checkout. Choose your country's store to see books available for purchase. But harnessing its potential requires knowledge of color science, systems, processing algorithms, and device characteristics-topics drawn from a broad range of disciplines. One can acquire the requisite background with an armload of physics, chemistry, engineering, computer science, and mathematics books and journals- or one can find it here, in the Digital Color Imaging Handbook. The first three chapters cover the basics of color vision, perception, and physics that underpin digital color imaging. The remainder of the text presents the technology of color imaging with chapters on color management, device color characterization, digital halftoning, image compression, color quantization, gamut mapping, computationally efficient transform algorithms, and color image processing for digital cameras. Supplemental materials are available for download from the CRC Web site, including electronic versions of some of the images presented in the book. Choose your country's store to see books available for purchase.

We appreciate your feedback. We'll publish them on our site once we've reviewed them. Celebrate Black Joy with these audiobooks and. Playwright Brad Fraser tells the story of his. “If you’re not changing your mind, you’re doin. View all posts You need a United States address to shop on our United States store. Go to our Russia store to continue. Examples of these components include image segmentation, enhancement, compres- sion, color characterization, and halftoning. Many of these topics are described in detail in other articles in this issue. T ypically, the components are individually designed and optimized, and the partitioning has the unintended consequence that engineers working on specific components acquire expertise limited to those components and think “within the box” for solving problems they encounter. Examples are specifically chosen to highlight concepts in color image processing, and a set of illustrative images is included with the examples. In our presentation, we assume that the reader is familiar with the basics of color imaging and device characteriza- tion. The following presents an overview of a color imaging system and its elements and then highlights techniques in the literature that attempt to account for system interactions for improved quality or performance. After that, presented in greater detail, are two specific examples of approaches that take into account interactions between elements that are nor- mally treated independently. Finally, concluding remarks are presented. COLOR IMAGING SYSTEM ELEMENTS The diagram of Figure 1 illustrates the components that are commonly identified as the build- ing blocks of an end-to-end digital color imaging system.Image capture devices, such as scanners and digital cameras, are used to capture and digitize these ana- log images. The capture step is followed by digital image pro- cessing elements that perform input related processing tasks.

For synthetic computer generated imagery, these are typically the first processing steps. The final step on the output side consists of image rendition on a printer or a dis- play and produces, as an end result, an image on a physical medium. The human visual system (HVS) is typically the end consumer of imagery generat- ed from the system, and in some scenarios, may be involved in comparing the original (or recollection thereof) with the output from the system. Characteristics of the HVS are therefore com- monly exploited throughout the imaging system. Note that this end-to-end representation of an imaging system is intended for conceptual understanding and for illustration of the interrelations among the individual elements. In practical systems, a single device may encapsulate a number of elements, including some that are not necessarily sequential, and not all the elements that are discussed here may be present in every imaging system. The design of digital color imaging systems incorporates some understanding of the over- all system elements and their interactions. In particular, for imagery intended for a human observer, the characteristics of the HVS influence the design of several of the components; spe- cific examples being the compression and halftoning operations that exploit the low-pass characteristics of the HVS. However, once the individual elements have been defined, they are often optimized individual- ly without regard to the interplay among the com- ponents. Digital imaging operations are listed in r ed text.

Comp ression Quantization Co lor Transform ation Descreening Se gm entation Co lor Ma nageme nt Pr inter Disp lay Paper Transparenc y Disp lay Paper Transparency Scene Sc anner Digital Camera Ph ys ical Medium Physical Medium Image Ca pture Im age Re ndition Im age Ar chival, Exc ha nge, Transmission Digital Input Pro cessing Co lor Ma nageme nt Ga mut Mapping Ha lf toning Digital Output Processing An alog Domain Digital Domain Human Vi sual S ystem Im age Synthesis A PRIMAR Y GOAL OF COLOR MANAGEMENT IN HARDCOPY IMAGING SYSTEMS IS TO ACHIEVE CONSISTENT AND ACCURA TE COLOR REPRODUCTION ACROSS DIFFERENT DEVICES. The following is a brief overview of some of these techniques. T wo detailed examples are then presented. A common theme in several of the approaches is the joint treatment of color and spatial dimensions, which are normally handled independently and sepa- rately. Consider the reproduction of JPEG-compressed color images on a printer. The stan- dard workflow is to first perform decompression, followed by a printer characterization trans- form that maps the input image, typically in a device-independent color space such as YC b C r, to printer cyan, magenta, yellow, and black (CMYK). The basic idea is to parse the printer characterization transform into an expensive three-dimensional (3-D) correction and a simple one-dimensional (1-D) correc - tion, and apply the former only to a small N. N subblock within each 8 ? 8 image block (N ? 8) in the discrete cosine transform domain afforded by the JPEG model. Observing that these images are then subsequently JPEG compressed, he proposes a method to synergistically combine the color correction and JPEG compres- sion in order to reduce the noise. T raditional methods to combat color moire involve carefully optimizing the halftone screen angles to minimize the visibility of the artifact. This adjustment occurs in the color characterization function executed prior to the halftoning step.

Thus, moire minimization is treated as an objective in the joint optimization of color characterization and halftoning. The former operation is typically referred to as color quantization, and the latter is a generalized halftoning step. The need for color quantization first arose in desktop displays with limited image memory for display. While most work in color quantization treats the color quantization and halftoning steps independently and sequential- ly, the performance can be improved by jointly optimizing these operations. They formulate a visually motivated cost function that encapsulates the combined cost of palettiza- tion and of the assignment of pixels to the palette colors. Efficient algorithms based on multiscale deterministic anneal- ing determine a spatially quan- tized image that optimizes the cost function. The results are a significant improvement over sequential processing, particu- larly for small thumbnails that are often necessary on mobile devices. Benefits from system optimization have also been realized in color output rendering for hardcopy through the use of techniques that consider printed color sepa- rations jointly rather than individually. While a similar concept has been the basis of analog halftone screens for lithographic printing for a long time, the issue has only recently been addressed in the context of the rectilinear grids necessitated by digital halftoning. Finally, we highlight one more example of joint spatial and color considerations applicable for scan to print applications involving halftone input and halftone reproduction. In such systems, the interaction between input and output halftones can generate objectionable rescreening moire even though the input sampling satisfies the well understood Nyquist sampling criterion.
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T raditionally, this problem has been addressed by WE SHOW THA T OPTIMIZA TION OF THE QUALITY AND PERFORMANCE OF A COLOR IMAGING SYSTEM IS INDEED A SYSTEM-WIDE PROBLEM THA T MUST T AKE INTO ACCOUNT INTERACTIONS AMONG THE V ARIOUS COMPONENTS. This model enables prediction of the output spatial image, which includes the halftone spatial structure and therefore also mim- ics the re-screening moire. Low-pass differences between the predicted output image and the desired image represent the pre- diction of the moire, which can be precompensated for in the input. The resulting algorithm may be understood in signal pro- cessing terms as a feedback compensation scheme as opposed to the heuristic low-pass filtering techniques. In the following, we present in some detail two specific examples that illustrate how the interplay among system ele- ments can be beneficially exploited in system optimization. LOW -COST COLOR LOOKUP T ABLE TRANSFORMS EXPLOITING SP A TIAL INTERACTIONS A primary goal of color management in hardcopy imaging sys- tems is to achieve consistent and accurate color reproduction across different devices. This necessitates the derivation and application of color correction transformations that correct for the nonlinear behavior of both the device and the HVS. The color transformations are typically complex multidimensional functions, which makes the real-time processing of image data a computationally prohibitive task. T o reduce computational cost, the functions are typically implemented as multidimensional lookup tables (LUT s). For hardware economy, the LUT s constitute a sparse sam- pling grid of nodes that partition the 3-D input color space into a set of rectangular cells. An example is shown in Figure 2 for a transformation from CIELAB to printer CMYK. Given an input color, the LUT transformation comprises two basic steps: 1) retrieval of a set of nodes lying on the enclosing rectangular cell and 2) 3-D interpolation among these nodes.

LUT s are invariably built on a regular rectangular grid so that the cell retrieval step can be performed with minimal computation. Most of the computational cost thus lies in the interpolation step. Many interpolation schemes exist. Of these, tetrahedral interpolation requires the fewest computa- tions, and all three require comparable storage and memory. For some applications, the computational cost of 3-D interpo- lation can be prohibitive. Examples of such applications include very high-speed printing and rendering on devices with limited computational resources. It is beneficial in these instances to improve the performance of the LUT transformation, while yet maintaining acceptable output quality. For each input color, exactly one of the neighboring nodes in the enclosing cell is selected for output based on a spatial dither mask. The basic idea behind these approaches is that the halftoning introduces a high-spatial frequency pattern that is least noticed (effectively averaged) by the visual system. In other words, the intermediate levels nor- mally obtained by interpolation are achieved via visual averaging of a high-spatial-frequency halftone pattern. The open circle is the input point to be mapped, and the surrounding black cir cles are the points used for interpolation.Second, for binary output devices, the impact on visual quality can be minimized by exploiting the fact that the output signals from the LUT undergo subsequent binary halfton- ing. This is, therefore, another good example of exploiting system interactions to improve performance. Assuming the input color space of the LUT transformation is a luminance-chromi- nance representation, multilevel halftoning is applied only in the two chrominance dimensions, while 1-D interpolation is applied in the luminance dimension. Figure 4 compares the output from standard 3-D tetrahedal interpolation with that of the proposed chrominance halftoning scheme.

The input image was represented in CIELAB coordi- nates and was converted to CMYK for a Xerox 5795 laser printer. The CMYK images were then converted to CIELAB using a printer model and then transformed to sRGB for inclu- sion in this document. Figure 4 is thus a soft proof of the printed output. (Note that reprinting of these images on an uncalibrated printer can produce addi- tional errors and artifacts. The reader is encouraged to judge color quality of the electronic version of this article on a standard CRT with characteristics similar to sRGB.) The differences between the standard and proposed techniques manifest themselves mostly as high-frequency chrominance textures in the smooth background regions. Recall that the image actually undergoes an additional binary halftoning step before printing. This step, which cannot be effectively simu- lated in the soft proofs in Figure 4, often serves to mask the high-frequency textures introduced by the multilevel chrominance halftoning. The luminance textures are more easily perceived by the HVS and more difficult to mask in the binary halftoning step. Finally, the textures can be reduced by simply increasing the size of the LUT along the dimensions in which multilevel halftoning is performed. T able 1 compares the computational cost of standard inter- polation techniques (tetrahedral and trilinear) with the pro- posed approach of chrominance halftoning in conjunction with 1-D luminance interpolation. It is evident that significant com- putational savings can be had with the proposed technique. The cost benefit comes from the fact that halftoning is far less com- putationally intensive than 3-D interpolation.In many systems, the characterization for input or output is optimized for only one medium and utilized for the mul- tiple different media used in prac- tice. This simplification occurs mainly because recharacterization for every new medium is time consuming and costly.

There still remains the challenge of associating, at the system level, the correct characterization profile for a given document based on the user’ s selection of medium. Using wider system knowledge, the problem for color scanners, in particular, can be mitigated. How this is done is highlighted in the following. The spatial characteristics of the input can aid the selection of an appropriate color characterization profile for color scan- ners. The technique relies on the fact that typical inputs for these devices are themselves color hardcopy reproductions from other imaging systems. The images in Figure 5 depict scans from images printed with different printing technologies, cap- tured at a resolution of 600 dpi. The scans correspond to small uniform regions in each print and are shown in a blown-up view here to illustrate the microstructure of the printed images. From the images, it is clear that their spatial structure identifies the printing technology. Since the photograph is a continuous tone process, its scan shows almost no spatial variation, while the scans from other technologies illustrate their halftone struc- ture. The principle of the technique is illustrated in Figure 6, where the estimated power spectra of the scanned images correspon- ding to the different marking technologies are shown. The estimated power spectra clearly highlight the differences among the printing processes. The halftone technologies are all readily distin- guished from the photograph by the much higher concentration of high frequency energy in the corresponding power spectra. Among the halftone technologies, the periodicity of halftones in lithographic and xerographic input images gives rise to sharp peaks in the power spectra at the locations of the halftone fre- quencies and their harmonics. These are therefore readily dis- tinguished from the inkjet input where no peaks are observed in the power spectrum (other than the dc component) because the halftones are aperiodic.

The aforementioned example illustrated how analysis of spatial characteristics of scanned images can be exploited for improving the accuracy of color characterization. In the example that follows, we illus- trate that an exact knowledge of the input can often enable, not only a recovery of color, but also the com- plete spectral reflectance from scan- ner RGB values. Figure 7 illustrates the concept. The scanner senses the reflectance distribution at its input and reduces it to a 3-D RGB representation as illustrated in the rightmost two blocks of Figure 7. In the absence of additional information, the 3-D color information is clearly insufficient for accurately reconstructing the object spectral reflectance r( ? ). In most color scanning applications, however, the original input image that is to be electronically captured is itself a reproduc- tion. This is the case, for instance, when a photographic print is to be scanned or a xerographically produced color document is to be copied. Since these reproductions are produced by exploit- ing the trichromacy of human vision, they are typically pro- duced by using only three independent color controls as illustrated by the rightmost two blocks of Figure 7. Even for four color, CMYK printing devices (and other devices employing process colors), typically only three independent controls are exercised in practice, and the situation of Figure 7 applies. This one-to-one nature arises because the color production systems are designed to produce different colors in response to different input control signals, and even though scanners have confusion among scanned spectra, the confusion exists only among spectra for different media. This is illustrated in Figure 8. Therefore, for the case where the input is known exactly, it is feasible to extract complete spectral reflectance data from scanner RGB data. We illustrate the technique in a more constrained setting, where a more direct signal processing solution is applicable.

Reflectance Spectrum.Thus, in density space, the input spectra for the scanner illus- trated in Figure 7 lie on a 3-D affine manifold. By exploiting additional system information, as we illustrate in the following, one can obtain an entirely model-based solution. If this constraint is used jointly with the constraint imposed by the model for the photographic medium, the spec- trum may be uniquely determined from the scanner RGB val- ues v. One challenge remaining, however, is a robust algorithm for solving these joint constraints. While both sets of con- straints are linear (technically, the constraint in density space is affine), they lie in different domains and therefore cannot be solved using simple linear algebra or by conventional convex set theoretic estimation schemes that require constraints to be convex in the same domain. The method of pro- jections onto convex sets (POCS) therefore provides a robust technique for solving the problem. Of course, practical use of the algorithm also requires knowledge of the spectral density D. Illustrative results from this technique are presented in Figure 9, where four esti- mated spectral reflectances (shown as broken lines) are com- pared against the corresponding measured values (shown as solid lines). From these plots, it is apparent that the technique provides accurate estimates of the spectra. The spectral calibration offers advantages over the conventional color calibration methods, in that the colorimetry under differ- ent viewing illuminants may be subsequently calculated, which is not possible if only a colorimetric calibration is available. CONCLUSIONS We have attempted to show, via several examples, that opti- mization of the quality and performance of a color imaging system is indeed a system-wide problem that must take into account interactions among the various components. In addi- tion to the examples described earlier, other instances of color imaging system interactions can be conceived.

In other cases, overall system performance is improved at no additional cost. Our second example illustrated this where the intelligent use of spatial side information obtained in the scanning process improved input color characterization accuracy. The article will hopefully provoke more engineers working on color imaging to also think in terms of systems and create addition- al examples of the benefits of such thinking. AUTHORS Raja Bala is a principal scientist in the Xerox Innovation Group, working on color imaging algorithms for Xerox's color products.He holds more than 30 patents and more than 40 publications in the field of color imaging. Gaurav Sharma is an associate professor in the Electrical and Computer Engineering Department and the Department of Biostatistics at the University of Rochester, New Y ork. His research interests include color imaging, multimedia security, and bioinformatics. He received a Ph.D. in electrical computer engineering from North Carolina State University.At the root of the CIE system are tristimulus, i.e. 3-tuple, representations of color that are a linear function of the spectral power distributions of light seen by the observer... We also note that color control functions do not naturally arise in the context of general polytopes but are related to zonotopal tilings. We remark on this relation after presenting our main results in the 5 In practice, this constraint is met, except in degenerate scenarios that are not particularly of interest because there exist infinitesimally small perturbations that dispel the degeneracy while causing only infinitesimal changes in any other quantities of interest.

The four-primary system P (4) w and the fiveprimary system P (5) w are obtained as extensions of the optimal systems P (3) V and P (4) V, respectively, by adding a primary whose chromaticity matches the display white chromaticity; specifically, P Figure 12 illustrates the tristimulus gamuts G for the 5 primary system P (5) w and the six primary system P. For multiprimary displays, the gamut, i.e., the range of colors that can be rendered using additive combinations of an arbitrary number of light sources (primaries) with modulated intensities, is known to be a zonotope, which is a specific type of convex polytope. Under the specific three-dimensional setting relevant for color representation and the constraint of physically meaningful nonnegative primaries, we develop a complete, cohesive, and directly usable mathematical characterization of the geometry of the multiprimary gamut zonotope that immediately identifies the surface facets, edges, and vertices and provides a parallelepiped tiling of the gamut. We relate the parallelepiped tilings of the gamut, that arise naturally in our characterization, to the flexibility in color control afforded by displays with more than four primaries, a relation that is further analyzed and completed in a Part II companion paper. We demonstrate several applications of the geometric representations we develop and highlight how the paper advances theory required for multiprimary display modeling, design, and color management and provides an integrated view of past work on on these topics. Additionally, we highlight how our work on gamut representations connects with and furthers the study of three-dimensional zonotopes in geometry. View Show abstract. The additive color mixture of two complementary colors creates the color white.