Empirical Evaluation of Boundary Policies for Wavelet-Based Image Coding

The wavelet transform has become the most interesting new algorithm for still image compression. Yet there are many parameters within a wavelet analysis and synthesis which govern the quality of a decoded image. In this paper, we discuss different image boundary policies and their implications for the decoded image. A pool of gray-scale images has been wavelet-transformed with different settings of the wavelet filter bank and quantization threshold and with three possible boundary policies. Our empirical evaluation is based on three benchmarks: a first judgement regards the perceived quality of the decoded image. The compression rate is a second crucial factor. Finally, the best parameter settings with regard to these two factors is weighted with the cost of implementation. Contrary to the new standard JPEG-2000, where mirror padding is implemented, our investigation proposes circular convolution as the boundary treatment

Wavelet Filter Evaluation for Image Coding

The wavelet transform has become the most interesting new algorithm for still image compression. Yet there are many parameters within a wavelet analysis and synthesis which govern the quality of a decoded image: decomposition strategy, image boundary policy, quantization threshold, etc. In this paper we discuss different image boundary policies and their implications for the decoded image. A focal point is the trade-off between the length of an orthogonal, compactly supported Daubechies-n wavelet filter bank and the decomposition depth of an image during analysis. An evaluation of the visual quality of images at different parameter settings leads to recommendations on the wavelet filter parameters to be used in image coding

A Wavelet Transform Applet for Interactive Learning

In recent years, new forms and techniques of teaching have appeared, based on the Internet and on multimedia applications. In the teleteaching Project Virtual University of the Upper Rhine Valley (VIROR), multimedia simulations and animations complement traditional teaching material. Lecturers use Java applets in their courses to explain complex structures. These are then stored in a multimedia database to enable asynchronous learning. The wavelet transform has become the most interesting new algorithm for still image compression. Yet, there are many parameters within a wavelet analysis and synthesis: choice of the wavelet filter bank, decomposition strategy, image boundary policy, quantization threshold, etc. We consider the wavelet transform to be a typical example of a complex, hard-to-understand algorithm that needs illustration by interactive multimedia. In this article, we present the didactic background and the implementation of a sample applet on the discrete wavelet transform, as taught in our multimedia course

Multimedia Applications of the Wavelet Transform

This dissertation investigates novel applications of the wavelet transform in the analysis and compression of audio, still images, and video. Most recently, some surveys have been published on the restoration of noisy audio signals. Based on these, we have developed a wavelet-based denoising program for audio signals that allows flexible parameter settings. The multiscale property of the wavelet transform can successfully be exploited for the detection of semantic structures in images: A comparison of the coefficients allows the extraction of a predominant structure. This idea forms the basis of our semiautomatic edge detection algorithm. Empirical evaluations and the resulting recommendations follow. In the context of the teleteaching project Virtual University of the Upper Rhine Valley (VIROR), many lectures were transmitted between remote locations. We thus encountered the problem of scalability of a video stream for different access bandwidths in the Internet. A substantial contribution of this dissertation is the introduction of the wavelet transform into hierarchical video coding and the recommendation of parameter settings based on empirical surveys. Furthermore, a prototype implementation proves the principal feasibility of a wavelet-based, nearly arbitrarily scalable application. Mathematical transformations constitute a commonly underestimated problem for students in their first semesters of study. Motivated by the VIROR project, we spent a considerable amount of time and effort on the exploration of approaches to enhance mathematical topics with multimedia; both the technical design and the didactic integration into the curriculum are discussed. In a large field trial on "traditional teaching versus multimedia-enhanced teaching", the objective knowledge gained by the students was measured. This allows us to objectively rate positive the efficiency of our teaching modules

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Wavelet Theory

The wavelet is a powerful mathematical tool that plays an important role in science and technology. This book looks at some of the most creative and popular applications of wavelets including biomedical signal processing, image processing, communication signal processing, Internet of Things (IoT), acoustical signal processing, financial market data analysis, energy and power management, and COVID-19 pandemic measurements and calculations. The editor’s personal interest is the application of wavelet transform to identify time domain changes on signals and corresponding frequency components and in improving power amplifier behavior

Measurements, Models, Systems and Design

531 s.