Hybrid Neural Network Predictive-Wavelet Image Compression System

This paper considers a novel image compression technique called hybrid predictive wavelet coding. The new proposed technique combines the properties of predictive coding and discrete wavelet coding. In contrast to JPEG2000, the image data values are pre-processed using predictive coding to remove interpixel redundancy. The error values, which are the difference between the original and the predicted values, are discrete wavelet coding transformed. In this case, a nonlinear neural network predictor is utilised in the predictive coding system. The simulation results indicated that the proposed technique can achieve good compressed images at high decomposition levels in comparison to JPEG2000

Image Compression Techniques: A Survey in Lossless and Lossy algorithms

The bandwidth of the communication networks has been increased continuously as results of technological advances. However, the introduction of new services and the expansion of the existing ones have resulted in even higher demand for the bandwidth. This explains the many efforts currently being invested in the area of data compression. The primary goal of these works is to develop techniques of coding information sources such as speech, image and video to reduce the number of bits required to represent a source without significantly degrading its quality. With the large increase in the generation of digital image data, there has been a correspondingly large increase in research activity in the field of image compression. The goal is to represent an image in the fewest number of bits without losing the essential information content within. Images carry three main type of information: redundant, irrelevant, and useful. Redundant information is the deterministic part of the information, which can be reproduced without loss from other information contained in the image. Irrelevant information is the part of information that has enormous details, which are beyond the limit of perceptual significance (i.e., psychovisual redundancy). Useful information, on the other hand, is the part of information, which is neither redundant nor irrelevant. Human usually observes decompressed images. Therefore, their fidelities are subject to the capabilities and limitations of the Human Visual System. This paper provides a survey on various image compression techniques, their limitations, compression rates and highlights current research in medical image compression

Investigation of Different Video Compression Schemes Using Neural Networks

Image/Video compression has great significance in the communication of motion pictures and still images. The need for compression has resulted in the development of various techniques including transform coding, vector quantization and neural networks. this thesis neural network based methods are investigated to achieve good compression ratios while maintaining the image quality. Parts of this investigation include motion detection, and weight retraining. An adaptive technique is employed to improve the video frame quality for a given compression ratio by frequently updating the weights obtained from training. More specifically, weight retraining is performed only when the error exceeds a given threshold value. Image quality is measured objectively, using the peak signal-to-noise ratio versus performance measure. Results show the improved performance of the proposed architecture compared to existing approaches. The proposed method is implemented in MATLAB and the results obtained such as compression ratio versus signalto- noise ratio are presented

On implementation and applications of the adaptive-network-based fuzzy inference system.

by Ong Kai Hin George.Thesis (M.Sc.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves [102-104])

Function approximation in high-dimensional spaces using lower-dimensional Gaussian RBF networks.

by Jones Chui.Thesis (M.Phil.)--Chinese University of Hong Kong, 1992.Includes bibliographical references (leaves 62-[66]).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Fundamentals of Artificial Neural Networks --- p.2Chapter 1.1.1 --- Processing Unit --- p.2Chapter 1.1.2 --- Topology --- p.3Chapter 1.1.3 --- Learning Rules --- p.4Chapter 1.2 --- Overview of Various Neural Network Models --- p.6Chapter 1.3 --- Introduction to the Radial Basis Function Networks (RBFs) --- p.8Chapter 1.3.1 --- Historical Development --- p.9Chapter 1.3.2 --- Some Intrinsic Problems --- p.9Chapter 1.4 --- Objective of the Thesis --- p.10Chapter 2 --- Low-dimensional Gaussian RBF networks (LowD RBFs) --- p.13Chapter 2.1 --- Architecture of LowD RBF Networks --- p.13Chapter 2.1.1 --- Network Structure --- p.13Chapter 2.1.2 --- Learning Rules --- p.17Chapter 2.2 --- Construction of LowD RBF Networks --- p.19Chapter 2.2.1 --- Growing Heuristic --- p.19Chapter 2.2.2 --- Pruning Heuristic --- p.27Chapter 2.2.3 --- Summary --- p.31Chapter 3 --- Application examples --- p.34Chapter 3.1 --- Chaotic Time Series Prediction --- p.35Chapter 3.1.1 --- Performance Comparison --- p.39Chapter 3.1.2 --- Sensitivity Analysis of MSE THRESHOLDS --- p.41Chapter 3.1.3 --- Effects of Increased Embedding Dimension --- p.41Chapter 3.1.4 --- Comparison with Tree-Structured Network --- p.46Chapter 3.1.5 --- Overfitting Problem --- p.46Chapter 3.2 --- Nonlinear prediction of speech signal --- p.49Chapter 3.2.1 --- Comparison with Linear Predictive Coding (LPC) --- p.54Chapter 3.2.2 --- Performance Test in Noisy Conditions --- p.55Chapter 3.2.3 --- Iterated Prediction of Speech --- p.59Chapter 4 --- Conclusion --- p.60Chapter 4.1 --- Discussions --- p.60Chapter 4.2 --- Limitations and Suggestions for Further Research --- p.61Bibliography --- p.6

Chaotic Time Series Forecasting Using Higher Order Neural Networks

This study presents a novel application and comparison of higher order neural networks (HONNs) to forecast benchmark chaotic time series. Two models of HONNs were implemented, namely functional link neural network (FLNN) and pi-sigma neural network (PSNN). These models were tested on two benchmark time series; the monthly smoothed sunspot numbers and the Mackey-Glass time-delay differential equation time series. The forecasting performance of the HONNs is compared against the performance of different models previously used in the literature such as fuzzy and neural networks models. Simulation results showed that FLNN and PSNN offer good performance compared to many previously used hybrid models