In response to the recent intensive needs for civilian security surveillance, both full and compact versions of a Multimedia Security Surveillance (MSS) system have been built up. The new Microsoft DirectShow technology was applied in implementing the multimedia stream-processing module. Through Microsoft Windows Driver Model interface, the chosen IEEE1394 enabled Fire-i cameras as external sensors are integrated with PC based continuous storage unit. The MSS application also allows multimedia broadcasting and remote controls. Cost analysis is included.

This study investigates the impact of multimedia on the effectiveness of information scanning. Information scanning is the act of seeking and receiving a wide variety of special information to develop a thorough understanding of the organization and the environment. The application domain of this study is Executive Support Systems. The experimental task is to identify potential threats and opportunities, a strategic information-scanning activity, based on the information stored in three ESS prototypes. Forty subjects from four organizations participated in the experiment. A random assignment process allocated them into three groups. The control group used the text-based ESS. The first experimental group used the visual multimedia ESS. The second experimental group used the audiovisual multimedia ESS. The experiment was carried out on the sites of the participating organizations. The investigator measured the effectiveness of information scanning based on the number of threats and opportunities each subject identifies. A close-ended questionnaire measured subjects' retention of information. The results of this study support the cognitive-fit theory. The findings indicate that multimedia is not an appropriate presentation format for analytical tasks. Subjects who use text-based ESS identify significantly more threats and opportunities than subjects who use audiovisual multimedia ESS. The cognitive style of subjects does not moderate the impact of multimedia. The results show that the use of multimedia does not necessarily improve retention of information. Further research is needed to determine the most effective combination of text, graphics, animation, video, and sound.

“A picture is worth a thousand words”. If this proverb were taken literally we all know that every person interprets images or photos differently in terms of its content. This is due to the semantics contained in these images. Content-based image retrieval has become a vast area of research in order to successfully describe and retrieve images according to the content. In military applications, intelligence images such as those obtained by the defence intelligence group are taken (mostly on film), developed and then manually annotated thereafter. These photos are then stored in a filing system according to certain attributes such as the location, content etc. To retrieve these images at a later stage might take days or even weeks to locate. Thus, the need for a digital annotation system has arisen. The images of the military contain various military vehicles and buildings that need to be detected, described and stored in a database. For our research we want to look at the effects that the rotation and elevation angle of an object in an image has on the retrieval performance. We chose model cars in order to be able to control the environment the photos were taken in such as the background, lighting, distance between the objects, and the camera etc. There are also a wide variety of shapes and colours of these models to obtain and work with. We look at the MPEG-7 descriptor schemes that are recommended by the MPEG group for video and image retrieval as well as implement three of them. For the military it could be required that when the defence intelligence group is in the field, that the images be directly transmitted via satellite to the headquarters. We have therefore included the JPEG2000 standard which gives a compression performance increase of 20% over the original JPEG standard. It is also capable to transmit images wirelessly as well as securely. Including the MPEG-7 descriptors that we have implemented, we have also implemented the fuzzy histogram and colour correlogram descriptors. For our experimentation we implemented a series of experiments in order to determine the effects that rotation and elevation has on our model vehicle images. Observations are made when each vehicle is considered separately and when the vehicles are described and combined into a single database. After the experiments are done we look at the descriptors and determine which adjustments could be made in order to improve the retrieval performance thereof. / Dr. W.A. Clarke

There is now more data being created than ever before and this data can be any form of data, textual, multimedia, spatial etc. To process this data, several big data processing platforms have been developed including Hadoop, based on the MapReduce model and LexisNexis’ HPCC systems. In this thesis we evaluate the HPCC Systems framework with a special interest in multimedia data analysis and propose a framework for multimedia data processing. It is important to note that multimedia data encompasses a wide variety of data including but not limited to image data, video data, audio data and even textual data. While developing a unified framework for such wide variety of data, we have to consider computational complexity in dealing with the data. Preliminary results show that HPCC can potentially reduce the computational complexity significantly. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2017. / FAU Electronic Theses and Dissertations Collection

Video identification or copy detection is a challenging problem and is becoming increasingly important with the popularity of online video services. The problem addressed in this thesis is the identification of a given video clip in a given set of videos. For a given query video, the system returns all the instance of the video in the data set. This identification system uses video signatures based on video tomography. A robust and low complexity video signature is designed and implemented. The nature of the signature makes it independent to the most commonly video transformations. The signatures are generated for video shots and not individual frames, resulting in a compact signature of 64 bytes per video shot. The signatures are matched using simple Euclidean distance metric. The results show that videos can be identified with 100% recall and over 93% precision. The experiments included several transformations on videos. / by Gustavo A. Leon. / Thesis (M.S.C.S.)--Florida Atlantic University, 2008. / Includes bibliography. / Electronic reproduction. Boca Raton, Fla., 2008. Mode of access: World Wide Web.

Recently, multimedia applications and their use have grown dramatically in popularity in strong part due to mobile device adoption by the consumer market. Applications, such as video conferencing, have gained popularity. These applications and others have a strong video component that uses the mobile device’s resources. These resources include processing time, network bandwidth, memory use, and battery life. The goal is to reduce the need of these resources by reducing the complexity of the coding process. Mobile devices offer unique characteristics that can be exploited for optimizing video codecs. The combination of small display size, video resolution, and human vision factors, such as acuity, allow encoder optimizations that will not (or minimally) impact subjective quality. The focus of this dissertation is optimizing video services in mobile environments. Industry has begun migrating from H.264 video coding to a more resource intensive but compression efficient High Efficiency Video Coding (HEVC). However, there has been no proper evaluation and optimization of HEVC for mobile environments. Subjective quality evaluations were performed to assess relative quality between H.264 and HEVC. This will allow for better use of device resources and migration to new codecs where it is most useful. Complexity of HEVC is a significant barrier to adoption on mobile devices and complexity reduction methods are necessary. Optimal use of encoding options is needed to maximize quality and compression while minimizing encoding time. Methods for optimizing coding mode selection for HEVC were developed. Complexity of HEVC encoding can be further reduced by exploiting the mismatch between the resolution of the video, resolution of the mobile display, and the ability of the human eyes to acquire and process video under these conditions. The perceptual optimizations developed in this dissertation use the properties of spatial (visual acuity) and temporal information processing (motion perception) to reduce the complexity of HEVC encoding. A unique feature of the proposed methods is that they reduce encoding complexity and encoding time. The proposed HEVC encoder optimization methods reduced encoding time by 21.7% and bitrate by 13.4% with insignificant impact on subjective quality evaluations. These methods can easily be implemented today within HEVC. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Wong Kit-pui. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 117-119). / ACKNOWLEDGMENT --- p.4 / ABSTRACT --- p.6 / KEYWORDS --- p.7 / Chapter Chapter 1 --- INTRODUCTION --- p.9 / Chapter Chapter 2 --- APPROACH --- p.14 / Chapter 2.1 --- Challenges --- p.15 / Chapter 2.2 --- Knowledge Representation --- p.16 / Chapter 2.3 --- Proposed Information Model --- p.17 / Chapter 2.4 --- Restricted Language Set --- p.20 / Chapter Chapter 3 --- THEORY --- p.26 / Chapter 3.1 --- Features --- p.26 / Chapter 3.1.1 --- Superficial Details --- p.30 / Chapter 3.1.2 --- Hidden Details --- p.31 / Chapter 3.2 --- Matching Process --- p.36 / Chapter 3.2.1 --- Inexact Match --- p.37 / Chapter 3.2.2 --- An Illustration --- p.38 / Chapter 3.2.2.1 --- Stage 1 - Query Parsing --- p.39 / Chapter 3.2.2.2 --- Stage 2 - Gross Filtering --- p.41 / Chapter 3.2.2.3 --- Stage 3 - Fine Scoring --- p.42 / Chapter 3.3 --- Extending Knowledge --- p.46 / Chapter 3.3.1 --- Attributes with Intermediate Closeness --- p.47 / Chapter 3.3.2 --- Comparing Different Entities --- p.48 / Chapter 3.4 --- Putting Concepts to Work --- p.50 / Chapter Chapter 4 --- IMPLEMENTATION --- p.52 / Chapter 4.1 --- Overall Structure --- p.53 / Chapter 4.2 --- Choosing NL Parser --- p.55 / Chapter 4.3 --- Ambiguity --- p.56 / Chapter 4.4 --- Storing Knowledge --- p.59 / Chapter 4.4.1 --- Type Hierarchy --- p.60 / Chapter 4.4.1.1 --- Node Name --- p.61 / Chapter 4.4.1.2 --- Node Identity --- p.61 / Chapter 4.4.1.3 --- Operations --- p.68 / Chapter 4.4.1.3.1 --- Direct Edit --- p.68 / Chapter 4.4.1.3.2 --- Interactive Edit --- p.68 / Chapter 4.4.2 --- Implicit Features --- p.71 / Chapter 4.4.3 --- Database of Captions --- p.72 / Chapter 4.4.4 --- Explicit Features --- p.73 / Chapter 4.4.5 --- Transformation Map --- p.74 / Chapter Chapter 5 --- ILLUSTRATION --- p.78 / Chapter 5.1 --- Gloss Tags --- p.78 / Chapter 5.2 --- Parsing --- p.81 / Chapter 5.2.1 --- Resolving Nouns and Verbs --- p.81 / Chapter 5.2.2 --- Resolving Adjectives and Adverbs --- p.84 / Chapter 5.2.3 --- Normalizing Features --- p.89 / Chapter 5.2.4 --- Resolving Prepositions --- p.90 / Chapter 5.3 --- Matching --- p.93 / Chapter 5.3.1 --- Gross Filtering --- p.94 / Chapter 5.3.2 --- Fine Scoring --- p.96 / Chapter Chapter 6 --- DISCUSSION --- p.101 / Chapter 6.1 --- Performance Measures --- p.101 / Chapter 6.1.1 --- General Parameters --- p.101 / Chapter 6.1.2 --- Experiments --- p.103 / Chapter 6.1.2.1 --- Inexact Matching Behaviour --- p.103 / Chapter 6.1.2.2 --- Exact Matching Behaviour --- p.106 / Chapter 6.2 --- Difficulties --- p.108 / Chapter 6.3 --- Possible Improvement --- p.110 / Chapter 6.4 --- Conclusion --- p.112 / REFERENCES --- p.117 / APPENDICES --- p.121 / Appendix A Notation --- p.121 / Appendix B Glossary --- p.123 / Appendix C Proposed Feature Slots and Value --- p.126 / Appendix D Sample Captions and Queries --- p.128 / Appendix E Manual Pages --- p.130 / Appendix F Directory Structure --- p.136 / Appendix G Imported Toolboxes --- p.137 / Appendix H Program Listing --- p.140

by Hau-Leung Chung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 41-42). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.5 / Chapter 1.1 --- Paper I --- p.5 / Chapter 1.2 --- Paper II --- p.6 / Chapter 2 --- The Inexact Newton-Like Method for Inverse Eigen- value Problem --- p.8 / Chapter 2.1 --- Introduction --- p.8 / Chapter 2.2 --- The Newton-Like Method --- p.9 / Chapter 2.3 --- The Inexact Newton-Like Method --- p.11 / Chapter 2.4 --- Convergence Analysis --- p.14 / Chapter 2.5 --- Numerical Experiments --- p.22 / Chapter 3 --- A DCT Based Watermarking Scheme for Copyright Protection of Images --- p.26 / Chapter 3.1 --- Introduction --- p.26 / Chapter 3.2 --- Preliminary --- p.28 / Chapter 3.2.1 --- Gray-level image --- p.28 / Chapter 3.2.2 --- Color image --- p.29 / Chapter 3.2.3 --- The Discrete Cosine transform --- p.30 / Chapter 3.3 --- Watermarking Approaches --- p.31 / Chapter 3.3.1 --- Insertion procedures --- p.31 / Chapter 3.3.2 --- Retrieval procedures --- p.33 / Chapter 3.4 --- Experimental results --- p.34 / Chapter 3.5 --- Other Applications --- p.38 / Chapter 3.5.1 --- Data Hiding --- p.38 / Chapter 3.5.2 --- Authentication --- p.39 / Chapter 3.5.3 --- Fingerprinting --- p.39 / Chapter 3.5.4 --- Copy Control --- p.39 / Chapter 3.6 --- Conclusion --- p.40 / Bibliography --- p.41

Wong Chung-Yu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaf 75). / Abstracts in English and Chinese. / Abstract --- p.ii / List of figures --- p.viii / List of tables --- p.ix / Chapter 1. --- BACKGROUND --- p.1 / Chapter 1.1 --- An analysis of manuals --- p.1 / Chapter 1.2 --- Existing practice --- p.5 / Chapter 1.3 --- New concept in dynamic manual --- p.7 / Chapter 1.3.1 --- Dynamic representation --- p.7 / Chapter 1.3.2 --- Machine-orientation --- p.8 / Chapter 2. --- DESIGN PHILOSOPHY --- p.10 / Chapter 2.1 --- Concept --- p.10 / Chapter 2.2 --- Data node --- p.12 / Chapter 2.3 --- Characteristic of function and operator --- p.14 / Chapter 2.4 --- Function hierarchy --- p.17 / Chapter 2.5 --- Manual-tree (conceptual and actual) --- p.18 / Chapter 2.6 --- Coding --- p.20 / Chapter 2.7 --- Operation sequence --- p.21 / Chapter 2.8 --- Parameter passing --- p.22 / Chapter 2.9 --- Manual-tree operation --- p.24 / Chapter 3. --- IMPLEMETATION OF DYNAMIC MANUAL SYSTEM --- p.25 / Chapter 3.1 --- Generator --- p.26 / Chapter 3.1.1 --- File format and generation --- p.26 / Chapter 3.2 --- Reader --- p.35 / Chapter 3.2.1 --- Guide service --- p.36 / Chapter 3.2.2 --- Button querying service --- p.37 / Chapter 3.2.3 --- Personal management service --- p.38 / Chapter 3.2.3.1 --- Insert --- p.38 / Chapter 3.2.3.2 --- Delete --- p.40 / Chapter 3.2.3.3 --- Swap --- p.40 / Chapter 3.3 --- The graphics --- p.42 / Chapter 4. --- EXPERIMENTS --- p.43 / Chapter 4.1 --- Experiment I (mobile phone) --- p.43 / Chapter 4.1.1 --- Data preparation --- p.43 / Chapter 4.1.2 --- Generating XML file --- p.46 / Chapter 4.1.3 --- Preparing multimedia material --- p.46 / Chapter 4.1.4 --- Design of Reader --- p.47 / Chapter 4.1.5 --- Testing --- p.51 / Chapter 4.1.5.1 --- Top-down search test --- p.51 / Chapter 4.1.5.2 --- Bottom-up search test --- p.52 / Chapter 4.1.5.3 --- function hierarchy modifying test --- p.53 / Chapter 4.2 --- Experiment II (pager) --- p.54 / Chapter 4.2.1 --- Data preparation --- p.54 / Chapter 4.2.2 --- Generating XML file --- p.54 / Chapter 4.2.3 --- Preparing multimedia material and the Reader --- p.56 / Chapter 4.2.4 --- Testing --- p.57 / Chapter 4.2.4.1 --- Top-down search test --- p.58 / Chapter 4.2.4.2 --- Bottom-up search test --- p.58 / Chapter 4.2.4.3 --- function hierarchy modifying test --- p.59 / Chapter 4.3 --- Control graphics constrain --- p.61 / Chapter 5. --- RESULTS --- p.65 / Chapter 5.1 --- Change of representation --- p.65 / Chapter 5.2 --- Storage and computation requirements --- p.70 / Chapter 6. --- CONCLUSION --- p.72 / References --- p.75 / Appendix A.1 input file of mobile phone (function part) --- p.76 / Appendix A.2 input file of mobile phone (operator part) --- p.96 / Appendix B.1 input file of pager (function part) --- p.105 / Appendix B.2 input file of pager (operator part) --- p.111 / Appendix C Function hierarchies and operator lists of the experiments --- p.113 / Appendix D Key words --- p.117

by Wan Wai Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references (leaves 77-[80]). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Wireless Multimedia System --- p.1 / Chapter 1.2 --- Motivation of this thesis --- p.2 / Chapter 1.3 --- The theme of this thesis --- p.4 / Chapter 1.3.1 --- System Model and Assumptions --- p.4 / Chapter 1.3.2 --- Outline of the thesis --- p.5 / Chapter 2 --- Overview of TDMA/FDMA Digital Cellular Systems --- p.7 / Chapter 2.1 --- The Cellular Concept --- p.7 / Chapter 2.2 --- Channel Assignment Strategies --- p.9 / Chapter 2.2.1 --- Fixed Channel Assignment --- p.9 / Chapter 2.2.2 --- Dynamic Channel Assignment --- p.9 / Chapter 2.3 --- Multiple Access Techniques --- p.10 / Chapter 2.3.1 --- Introduction to Multiple Access --- p.10 / Chapter 2.3.2 --- Frequency Division Multiple Access - FDMA --- p.11 / Chapter 2.3.3 --- Time Division Multiple Access - TDMA --- p.12 / Chapter 2.4 --- A TDMA/FDMA System - GSM --- p.13 / Chapter 2.4.1 --- Global System for Mobile --- p.13 / Chapter 2.4.2 --- GSM radio subsystem --- p.13 / Chapter 3 --- Multi-rate Data in TDMA/FDMA Digital Cellular Systems --- p.17 / Chapter 3.1 --- Incorporation of Multimedia Data --- p.17 / Chapter 3.2 --- A Global Optimal Strategy --- p.19 / Chapter 3.2.1 --- Channel Rearrangement --- p.19 / Chapter 3.2.2 --- Analytical Performance Analysis of a Special Case --- p.21 / Chapter 3.2.3 --- Numerical Results --- p.24 / Chapter 3.2.4 --- Issues in Channel Rearrangement --- p.25 / Chapter 4 --- Multiple Slots Allocations --- p.26 / Chapter 4.1 --- Introduction --- p.26 / Chapter 4.2 --- No-Split Algorithm --- p.27 / Chapter 4.2.1 --- No-Split Algorithm --- p.27 / Chapter 4.2.2 --- Pros and Cons --- p.28 / Chapter 4.3 --- Best Fit Algorithm --- p.29 / Chapter 4.3.1 --- Best Fit Algorithm --- p.29 / Chapter 4.3.2 --- Optimization --- p.31 / Chapter 4.3.3 --- Pros and Cons --- p.32 / Chapter 4.4 --- Comparison of the two algorithms --- p.32 / Chapter 5 --- Buddy Algorithm --- p.37 / Chapter 5.1 --- Introduction --- p.37 / Chapter 5.2 --- Buddy System in Memory Management --- p.38 / Chapter 5.3 --- Buddy Algorithm --- p.40 / Chapter 5.3.1 --- Adaptation in slot allocation --- p.40 / Chapter 5.3.2 --- Data structure --- p.40 / Chapter 5.3.3 --- Slot allocation --- p.40 / Chapter 5.3.4 --- Slot deallocation --- p.44 / Chapter 5.4 --- Inference Property --- p.45 / Chapter 5.4.1 --- Proof of the Inference Property --- p.47 / Chapter 5.5 --- Pros and Cons --- p.49 / Chapter 6 --- Performance Study --- p.51 / Chapter 6.1 --- Introduction --- p.51 / Chapter 6.2 --- Fixed Channel Assignment --- p.52 / Chapter 6.2.1 --- System Parameters --- p.52 / Chapter 6.2.2 --- Simulation Results --- p.53 / Chapter 6.3 --- Dynmaic Channel Assignment --- p.55 / Chapter 6.3.1 --- System Parameters --- p.55 / Chapter 6.3.2 --- Simulation Results --- p.56 / Chapter 7 --- A Case Study - H.263 Video Coding --- p.59 / Chapter 7.1 --- CCITT H.263 Image Compression --- p.59 / Chapter 7.2 --- On a GSM Network --- p.60 / Chapter 8 --- Conclusion --- p.63 / Chapter A --- A General Data + Voice System with Channel Rearrangement --- p.65 / Chapter A.1 --- System Model --- p.65 / Chapter A.2 --- Markovian Analysis --- p.66 / Chapter B --- NP-Completeness Proof of the Best Fit Algorithm --- p.69 / Chapter B.1 --- CONSTRAINT SUBSET-SUM Problem --- p.69 / Chapter B.2 --- BEST-FIT Problem --- p.72 / Chapter C --- Proof of Proposition 5.2 --- p.74 / Chapter C.1 --- Upper Bound on Demand Advancement --- p.74 / Chapter C.2 --- Proof of Proposition 5.2 --- p.75 / Bibliography --- p.77