Gustav J. Braun

Digital Image Display: Algorithms and Implementation

Series Editors Foreword.Preface.Acknowledgments.Abbreviations and Acronyms.1 Fundamentals of Digital Image Display.2 Digital Filters for Image Rescaling.3 Common Format Conversions, Artifacts, and Test Images.4 Alternatives to Orthogonal Separable Digital Rescaling Methods.5 Display-Specific Algorithms for Enhancing Image Quality.6 The Importance of the Sequence of Operations in Digital Image Display.7 Nonlinear Image Rescaling: Digital Image Warping.8 Digital Video Display.9 Digital Video Deinterlacing/Interlacing.10 Applications, Hot Issues, and Trends in Digital Image Display.Appendix A.Bibliography.Index.

Displaying images on mobile devices: capabilities, issues, and solutions

Wireless imaging is enabling visual communication ‘anytime anywhere’ to become a reality. Apart from wireless communication issues, a key technical challenge is how to achieve the best-perceived image quality given the limited screen size and display bit-depth of the mobile devices. In this paper, we give an overview of the current capabilities of various mobile devices, highlight some of the technical issues, and present potential solutions. In addition, we present a review of some of the software products on the market and look ahead to the trend toward more capable devices. Copyright

Visual display characterization using flicker photometry techniques

A complete colorimetric characterization of a cathode ray tube (CRT) display was generated using an all-visual psychophysical measurement procedure. This process requires no external props or matching stimuli and uses a three-stage approach for estimating the display’s (1) system nonlinearities (i.e., gamma), (2) channel luminance ratios (i.e., white-point setting), and (3) phosphor chromaticity matrix. Simple homochromatic brightness matching experiments are used to solve the system nonlinearities using either spatially or temporally modulated stimuli. A novel heterochromatic minimum-flicker technique is presented for estimating the relative luminance contributions of each display channel. Finally, a chromaticity selection process is used to choose an appropriate chromaticity set for the display. These components are used to populate a model of the colorimetric mixing characteristics of the display device. The visually populated display model was compared to an instrumentation-based process. The results of this analysis showed that the all-visual characterization process compared favorably to the instrumentation-based model. For 12 subjects, the mean colorimetric errors (averaged across subjects) for a typical color-management task were approximately 2 CIE ΔE94 units with maximum colorimetric errors (averaged across subjects) of approximately 4 CIE ΔE94 units.