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

Contemporary Affirmation of SPIHT Improvements in Image Coding

Set partitioning in hierarchal trees (SPIHT) is actually a widely-used compression algorithm for wavelet altered images. On most algorithms developed, SPIHT algorithm from the time its introduction in 1996 for image compression has got lots of interest. Though SPIHT is considerably simpler and efficient than several present compression methods since it's a completely inserted codec, provides good image quality, large PSNR, optimized for modern image transmission, efficient conjunction with error defense, form information on demand and hence element powerful error correction decreases from starting to finish but still it has some downsides that need to be taken away for its better use therefore since its development it has experienced many adjustments in its original model. This document presents a survey on several different improvements in SPIHT in certain fields as velocity, redundancy, quality, error resilience, sophistication, and compression ratio and memory requirement

An Enhanced Wavelet based Image Compression Technique

With the fast expansion of multimedia technologies, the compression of multimedia data has become an important aspect. Image compression is important for efficient storage and transmission of images. The limitation in bandwidth of wireless channels has made data compression a necessity. Wireless channels are bandwidth limited and due to this constraint of wireless channels, progressive image transmission has gained much popularity and acceptance. The Embedded Zerotree Wavelet algorithm (EZW) is based on progressive encoding, in which bits in the bit stream are generated in order of importance. The EZW algorithm, code all the frequency band of wavelet coefficients as the same importance without considering the amount of information in each frequency band. This paper presents an enhanced wavelet based approach to overcome the limitation of the Embedded Zerotree Wavelet (EZW) algorithm. This method divides the image into some sub-blocks

Empirical analysis of BWT-based lossless image compression

The Burrows-Wheeler Transformation (BWT) is a text transformation algorithm originally designed to improve the coherence in text data. This coherence can be exploited by compression algorithms such as run-length encoding or arithmetic coding. However, there is still a debate on its performance on images. Motivated by a theoretical analysis of the performance of BWT and MTF, we perform a detailed empirical study on the role of MTF in compressing images with the BWT. This research studies the compression performance of BWT on digital images using different predictors and context partitions. The major interest of the research is in finding efficient ways to make BWT suitable for lossless image compression.;This research studied three different approaches to improve the compression of image data by BWT. First, the idea of preprocessing the image data before sending it to the BWT compression scheme is studied by using different mapping and prediction schemes. Second, different variations of MTF were investigated to see which one works best for Image compression with BWT. Third, the concept of context partitioning for BWT output before it is forwarded to the next stage in the compression scheme.;For lossless image compression, this thesis proposes the removal of the MTF stage from the BWT compression pipeline and the usage of context partitioning method. The compression performance is further improved by using MED predictor on the image data along with the 8-bit mapping of the prediction residuals before it is processed by BWT.;This thesis proposes two schemes for BWT-based image coding, namely BLIC and BLICx, the later being based on the context-ordering property of the BWT. Our methods outperformed other text compression algorithms such as PPM, GZIP, direct BWT, and WinZip in compressing images. Final results showed that our methods performed better than the state of the art lossless image compression algorithms, such as JPEG-LS, JPEG2000, CALIC, EDP and PPAM on the natural images

Enhancements to SPIHT-based and CSPIHT-based coders

Master'sMASTER OF ENGINEERIN

Compression Technologies of Graphic Information

The classification of types of information redundancy in symbolic and graphical forms representation of information is done. The general classification of compression technologies for graphical information is presented as well. The principles of design, tasks and variants for realizations of semantic compression technology of graphical information are suggested

3D oceanographic data compression using 3D-ODETLAP

This paper describes a 3D environmental data compression technique for oceanographic datasets. With proper point selection, our method approximates uncompressed marine data using an over-determined system of linear equations based on, but essentially different from, the Laplacian partial differential equation. Then this approximation is refined via an error metric. These two steps work alternatively until a predefined satisfying approximation is found. Using several different datasets and metrics, we demonstrate that our method has an excellent compression ratio. To further evaluate our method, we compare it with 3D-SPIHT. 3D-ODETLAP averages 20% better compression than 3D-SPIHT on our eight test datasets, from World Ocean Atlas 2005. Our method provides up to approximately six times better compression on datasets with relatively small variance. Meanwhile, with the same approximate mean error, we demonstrate a significantly smaller maximum error compared to 3D-SPIHT and provide a feature to keep the maximum error under a user-defined limit