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

Compression of Satellite Imagery Sequences Using Wavelet for Detection of Natural Disaster

Indonesia, geographically and geologically, is potentially encounter natural disasters. One of the tools used to early detect disaster is sensor of ocean waves change, but it has drawbacks including the time difference between information/warnings obtained with the disaster event is very short, less than 30 minutes. The faster detector is required, so the time difference will be longer. For example, early detection of natural disasters information system, which can be made with the pattern recognition of satellite imagery sequences of before and during natural disaster images. This study was conducted to determine the right wavelet to compress the satellite image sequences. The compressed images will be used to perform the pattern recognition of natural disaster using artificial neural network. This study use satellite imagery sequences of tornadoes and hurricanes. The eight wavelets used are Haar, Coiflet 1, Coiflet 3, Symlet 2, Symlet 5, 1 AJS, AJS 2, and AJS 3. The test results are then compared with the compression ratio. Result of this study are the comparison of wavelet used to compress satellite imagery sequences, which is save the storage space, access time, processing time and delivery time

Natural Disaster Detection Using Wavelet and Artificial Neural Network

Indonesia, by the location of its geographic and geologic, it have more potential encounters for natural disasters. This nation is traversed by three tectonic plates, namely: IndoAustralian, the Eurasian and the Pacific plates. One of the tools employed to detect danger and send an early disaster warning is sensor device for ocean waves, but it has drawbacks related to the very limited time gap between information/warnings obtained and the real disaster event, which is only less than 30 minutes. Natural disaster early detection information system is essential to prevent potential danger. The system can make use of the pattern recognition of satellite imagery sequences that take place before and during the natural disaster. This study is conducted to determine the right wavelet to compress the satellite image sequences and to perform the pattern recognition process of a natural disaster employing an artificial neural network. This study makes use of satellite imagery sequences of tornadoes and hurricanes

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