Depth-first search embedded wavelet algorithm for hardware implementation

The emerging technology of image communication over wireless transmission channels requires several new challenges to be simultaneously met at the algorithm and architecture levels. At the algorithm level, desirable features include high coding performance, bit stream scalability, robustness to transmission errors and suitability for content-based coding schemes. At the architecture level, we require efficient architectures for construction of portable devices with small size and low power consumption. An important question is to ask if a single coding algorithm can be designed to meet the diverse requirements. Recently, researchers working on improving different features have converged on a set of coding schemes commonly known as embedded wavelet algorithms. Currently, these algorithms enjoy the highest coding performances reported in the literature. In addition, embedded wavelet algorithms have the natural feature of being able to meet a target bit rate precisely. Furthermore work on improving the algorithm robustness has shown much promise. The potential of embedded wavelet techniques has been acknowledged by its inclusion in the new JPEG2000 and MPEG-4 image and video coding standards

Zooplankton visualization system: design and real-time lossless image compression

In this thesis, I present a design of a small, self-contained, underwater plankton imaging system. I base the imaging system’s design on an embedded PC architecture based on PC/104-Plus standards to meet the compact size and low power requirements. I developed a simple graphical user interface to run on a real-time operating system to control the imaging system. I also address how a real-time image compression scheme implemented on an FPGA chip speeds up image transfer speeds of the imaging system. Since lossless compression of the image is required in order to retain all image details, I began with an established compression scheme like SPIHT, and latter proposed a new compression scheme that suits the imaging system’s requirements. I provide an estimate of the total amount of resources required and propose suitable FPGA chips to implement the compression scheme. Finally, I present various parallel designs by which the FPGA chip can be integrated into the imaging system

Implementation of Image Compression Algorithm using Verilog with Area, Power and Timing Constraints

Image compression is the application of Data compression on digital images. A fundamental shift in the image compression approach came after the Discrete Wavelet Transform (DWT) became popular. To overcome the inefficiencies in the JPEG standard and serve emerging areas of mobile and Internet communications, the new JPEG2000 standard has been developed based on the principles of DWT. An image compression algorithm was comprehended using Matlab code, and modified to perform better when implemented in hardware description language. Using Verilog HDL, the encoder for the image compression employing DWT was implemented. Detailed analysis for power, timing and area was done for Booth multiplier which forms the major building block in implementing DWT. The encoding technique exploits the zero tree structure present in the bitplanes to compress the transform coefficients

Discrete Wavelet Transforms

The discrete wavelet transform (DWT) algorithms have a firm position in processing of signals in several areas of research and industry. As DWT provides both octave-scale frequency and spatial timing of the analyzed signal, it is constantly used to solve and treat more and more advanced problems. The present book: Discrete Wavelet Transforms: Algorithms and Applications reviews the recent progress in discrete wavelet transform algorithms and applications. The book covers a wide range of methods (e.g. lifting, shift invariance, multi-scale analysis) for constructing DWTs. The book chapters are organized into four major parts. Part I describes the progress in hardware implementations of the DWT algorithms. Applications include multitone modulation for ADSL and equalization techniques, a scalable architecture for FPGA-implementation, lifting based algorithm for VLSI implementation, comparison between DWT and FFT based OFDM and modified SPIHT codec. Part II addresses image processing algorithms such as multiresolution approach for edge detection, low bit rate image compression, low complexity implementation of CQF wavelets and compression of multi-component images. Part III focuses watermaking DWT algorithms. Finally, Part IV describes shift invariant DWTs, DC lossless property, DWT based analysis and estimation of colored noise and an application of the wavelet Galerkin method. The chapters of the present book consist of both tutorial and highly advanced material. Therefore, the book is intended to be a reference text for graduate students and researchers to obtain state-of-the-art knowledge on specific applications

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin