Wavelet Coding of Images: Adaptation, Scalability, and Transmission over Wireless Channels

Abstract

In this dissertation, we study the problem of image compression for storage and transmission applications separately. In addition to proposing new image coding systems, we consider different design constraints such as complexity and scalability. We propose a new classification scheme, dubbed spectral classification, which uses the spectral characteristics of the image blocks to classify them into one of a finite number of classes. The spectral classifier is used in adaptive image coding based on the discrete wavelet transform and shown to outperform gain-based classifiers while requiring a lower computational complexity. The resulting image coding system provides one of the best available rate-distortion performances in the literature. Also, we introduce a family of multiresolution image coding systems with different constraints on the complexity. For the class of rate-scalable image coding systems, we address the problem of progressive transmission and propose a method for fast reconstruction of a subband-decomposed progressively transmitted image. Another important problem studied in this dissertation is the transmission of images over noisy channels, especially for the wireless channels in which the characteristics of the channel is time-varying. We propose an adaptive rate allocation scheme to optimally choose the rates of the source coder and channel coder pair in a tandem source-channel coding framework. Also, we suggest two adaptive coding systems for quantization and transmission over a finite-state channel using a combined source and channel coding scheme. Finally, we develop simple table- lookup encoders to reduce the complexity of channel-optimized quantizers while providing a slightly inferior performance. We propose the use of lookup tables for transcoding in heterogeneous networks