Theory, design and applications of linear transforms for information transmission

The aim of this dissertation is to study the common features of block transforms, subband filter banks, and wavelets, and demonstrate how discrete uncertainty can be applied to evaluate these different decomposition techniques. In particular, we derive an uncertainty bound for discrete-time functions. It is shown that this bound is the same as that for continuous-time functions, if the discrete-time functions have a certain degree of regularity. This dissertation also deals with spectral modeling in filter banks. It is shown, both theoretically and experimentally, that subspectral modeling is superior to full spectrum modeling if performed before the rate change. The price paid for this performance improvement is an increase of computations. A few different signal sources were considered in this study. It is shown that the performances of AR and ARMA modeling techniques are comparable in subspectral modeling. The first is desired because of its simplicity. As an application of AR modeling, a coding algorithm of speech, namely CELP embedded in a filter bank structure was also studied. We found that there were no improvements of subband CELP technique over the full band one. The theoretical reasonings of the experimental results are also given. This dissertation also addresses the problems of what type of transform to be used and to what extent an image should be decomposed. To this aim, an objective and subjective evaluations of different transform bases were done. We propose a smart algorithm for the decomposition of a channel into its sub-channels in the discrete multitone communications. This algorithm evaluates the unevenness and energy distribution of the channel spectrum in order to get its Variable adaptive partitioning. It is shown that the proposed algorithm leads to a near optimal performance of the discrete multitone transceiver. This flexible splitting of the channel suffers less from the aliasing problem that exists in blind decompositions using fixed transforms. This dissertation extends the discrete multitone to the flexible multiband concept which brings significant performance improvements for digital communications

Quantization of multiresolution transform coefficients for high compression of digital images

New developments in transformation theory have fueled interest in methods that employ transformation in the computation process. Theory from various disciplines including electrical engineering, physics, mathematics, and computer science have benefited from these advances. The greatest impact in computer science by these methods is in the area of image compression. Digital image compression is currently of high interest in computer science. The growing demand for images in computers has grown faster than the technology and thus solutions are sought. This work deals with the problem of quantization of resultant coefficients of the transforms in compression methods that perform transformation of the data. The digital image data transformations include quadrature mirror filtering, conjugate quadrature filtering, and wavelet methods. The process of transformation may be implemented in a reversible manner such that no change in the data is present. Quantization does not enjoy this luxury and implementation of a quantization scheme should be a careful and precise process. Various transformation processes are examined and the resultant data from the multiresolution sub-band coding process is targeted by quantization methods developed for compression of the data

Multimedia applications of three-dimensional digital filters.

Digital signal processing has long been an extremely important field of study. One-dimensional and two-dimensional filters have applications in areas such as audio filtering or image processing respectively. As VLSI technology continues to increase, higher-dimensional digital filters are becoming more practical. This thesis investigates the application of Three-Dimensional (3-D) Digital Filters to the area of multimedia. Specifically, it investigates the use of 3-D Interpolation filters to increase the horizontal, vertical, and temporal resolution, or frame rate, of a moving image sequence. The thesis begins by presenting the theory of digital interpolation in one dimension, and then extends that theory to three dimensions. Next the theory is presented for the design of a filter with appropriate characteristics for filtering a video image; i.e. near-linear phase and a steep transition band. After the basic theory is presented, a plan for implementing the filtering of a video image in software is presented along with the relevant file format information. Results from this implementation are shown next, and the thesis ends with a summary and conclusions.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2000 .M34. Source: Masters Abstracts International, Volume: 40-04, page: 1048. Adviser: M. A. Sid-Ahmed. Thesis (M.A.Sc.)--University of Windsor (Canada), 2000

Fusing spatial and temporal components for real-time depth data enhancement of dynamic scenes

The depth images from consumer depth cameras (e.g., structured-light/ToF devices) exhibit a substantial amount of artifacts (e.g., holes, flickering, ghosting) that needs to be removed for real-world applications. Existing methods cannot entirely remove them and perform slow. This thesis proposes a new real-time spatio-temporal depth image enhancement filter that completely removes flickering and ghosting, and significantly reduces holes. This thesis also presents a novel depth-data capture setup and two data reduction methods to optimize the performance of the proposed enhancement method

Video Indexing and Retrieval Techniques Using Novel Approaches to Video Segmentation, Characterization, and Similarity Matching

Multimedia applications are rapidly spread at an ever-increasing rate introducing a number of challenging problems at the hands of the research community, The most significant and influential problem, among them, is the effective access to stored data. In spite of the popularity of keyword-based search technique in alphanumeric databases, it is inadequate for use with multimedia data due to their unstructured nature. On the other hand, a number of content-based access techniques have been developed in the context of image indexing and retrieval; meanwhile video retrieval systems start to gain wide attention, This work proposes a number of techniques constituting a fully content-based system for retrieving video data. These techniques are primarily targeting the efficiency, reliability, scalability, extensibility, and effectiveness requirements of such applications. First, an abstract representation of the video stream, known as the DC sequence, is extracted. Second, to deal with the problem of video segmentation, an efficient neural network model is introduced. The novel use of the neural network improves the reliability while the efficiency is achieved through the instantaneous use of the recall phase to identify shot boundaries. Third, the problem of key frames extraction is addressed using two efficient algorithms that adapt their selection decisions based on the amount of activity found in each video shot enabling the selection of a near optimal expressive set of key frames. Fourth, the developed system employs an indexing scheme that supports two low-level features, color and texture, to represent video data, Finally, we propose, in the retrieval stage, a novel model for performing video data matching task that integrates a number of human-based similarity factors. All our software implementations are in Java, which enables it to be used across heterogeneous platforms. The retrieval system performance has been evaluated yielding a very good retrieval rate and accuracy, which demonstrate the effectiveness of the developed system

A GPU-based, three-dimensional level set solver with curvature flow

technical reportLevel set methods are a powerful tool for implicitly representing deformable surfaces. Since their inception, these techniques have been used to solve prob- lems in fields as varied as computer vision, scientific visualization, computer graphics and computational physics. With the power and flexibility of this approach; however, comes a large computational burden. In the level set ap- proach, surface motion is computed via a partial differential equation (PDE) framework. One possibility for accelerating level-set based applications is to map the solver kernel onto a commodity graphics processing unit (GPU). GPUs are parallel, vector computers whose power is currently increasing at a faster rate than that of CPUs. in this work, we demonstrate a GPU-based, three- dimensional level set solver that is capable of computing curvature flow as well as other speed terms. Results are shown for this solver segmenting the brain surface from an MRI data set

Quality-oriented adaptation scheme for multimedia streaming in local broadband multi-service IP networks

The research reported in this thesis proposes, designs and tests the Quality-Oriented Adaptation Scheme (QOAS), an application-level adaptive scheme that offers high quality multimedia services to home residences and business premises via local broadband IP-networks in the presence of other traffic of different types. QOAS uses a novel client-located grading scheme that maps some network-related parameters’ values, variations and variation patterns (e.g. delay, jitter, loss rate) to application-level scores that describe the quality of delivery. This grading scheme also involves an objective metric that estimates the end-user perceived quality, increasing its effectiveness. A server-located arbiter takes content and rate adaptation decisions based on these quality scores, which is the only information sent via feedback by the clients. QOAS has been modelled, implemented and tested through simulations and an instantiation of it has been realized in a prototype system. The performance was assessed in terms of estimated end-user perceived quality, network utilisation, loss rate and number of customers served by a fixed infrastructure. The influence of variations in the parameters used by QOAS and of the networkrelated characteristics was studied. The scheme’s adaptive reaction was tested with background traffic of different type, size and variation patterns and in the presence of concurrent multimedia streaming processes subject to user-interactions. The results show that the performance of QOAS was very close to that of an ideal adaptive scheme. In comparison with other adaptive schemes QOAS allows for a significant increase in the number of simultaneous users while maintaining a good end-user perceived quality. These results are verified by a set of subjective tests that have been performed on viewers using a prototype system

A Family of Hierarchical Encoding Techniques for Image and Video Communications

As the demand for image and video transmission and interactive multimedia applications continues to grow, scalable image and video compression that has robust behavior over unreliable channels are of increasing interest. These desktop applications require scalability as a main feature due to its heterogeneous nature, since participants in an interactive multimedia application have different needs and processing power. Also, the encoding and decoding algorithm complexity must be low due to the practical considerations of low-cost low-power receiver terminals. This requires image and video encoding techniques that jointly considers compression, scalability, robustness, and simplicity. In this dissertation, we present a family of image and video-encoding techniques, which are developed to support conferencing applications. We achieve scalability, robustness and low computational complexity by building our encoding techniques based on the quadtree and octree representation methods. First we developed an image encoding technique using the quadtree representation of images and vector quantization. We use a mean-removal technique to separate the means image and the difference image. The difference image is then encoded as a breadth first traversal of the quadtree corresponding to the image. Vector quantization is then used to compress the quadtree nodes based on the spatial locality of the quadtree data. Our next step was to use the quadtree-based image encoding technique as a base for developing a differential video encoding technique. We extended it to encode video by applying the well-known IPB technique to the image encoding system. Then, we explore another method of extending our image encoding technique to encode video streams. The basic idea was to use exactly the same three steps used in our image encoding technique, mean removal, conversion to tree structure, and vector quantization, and replace the quadtree structure with an octree structure. The octree is the three-dimensional equivalent of the quadtree. We divide the sequence of frames into groups and view each group as a three-dimensional object. By encoding frames together, we can obtain substantial savings in encoding time and better compression results. Finally, we combined both the differential quadtree and octree approaches to generate a new hybrid encoding technique. We encode one frame using the quadtree-based image encoding technique, and then encode the following group of frames as a differential octree based upon the first frame. Using a set of experiments, the quadtree-based image encoding and differential video encoding techniques were shown to provide reasonable compression in comparison with similar techniques, while the octree and hybrid video encoding techniques gave impressive compression results. Furthermore, we demonstrated that our encoding techniques are time efficient compared to the more common frequency based techniques. We also compare their scalability feature favorably with other well-known scalable techniques. Moreover, we demonstrated their ability to tolerate and conceal error. The new encoding techniques proved to be efficient methods of encoding for interactive multimedia applications

Interactive visualization of computational fluid dynamics data.

This thesis describes a literature study and a practical research in the area of flow visualization, with special emphasis on the interactive visualization of Computational Fluid Dynamics (CFD) datasets. Given the four main categories of flow visualization methodology; direct, geometric, texture-based and feature-based flow visualization, the research focus of our thesis is on the direct, geometric and feature-based techniques. And the feature-based flow visualization is highlighted in this thesis. After we present an overview of the state-of-the-art of the recent developments in the flow visualization in higher spatial dimensions (2.5D, 3D and 4D), we propose a fast, simple, and interactive glyph placement algorithm for investigating and visualizing boundary flow data based on unstructured, adaptive resolution boundary meshes from CFD dataset. Afterward, we propose a novel, automatic mesh-driven vector field clustering algorithm which couples the properties of the vector field and resolution of underlying mesh into a unified distance measure for producing high-level, intuitive and suggestive visualization of large, unstructured, adaptive resolution boundary CFD meshes based vector fields. Next we present a novel application with multiple-coordinated views for interactive information-assisted visualization of multidimensional marine turbine CFD data. Information visualization techniques are combined with user interaction to exploit our cognitive ability for intuitive extraction of flow features from CFD datasets. Later, we discuss the design and implementation of each visualization technique used in our interactive flow visualization framework, such as glyphs, streamlines, parallel coordinate plots, etc. In this thesis, we focus on the interactive visualization of the real-world CFD datasets, and present a number of new methods or algorithms to address several related challenges in flow visualization. We strongly believe that the user interaction is a crucial part of an effective data analysis and visualization of large and complex datasets such as CFD datasets we use in this thesis. In order to demonstrate the use of the proposed techniques in this thesis, CFD domain experts reviews are also provided