

@mastersthesis {6490,
	title = {The {Study} of wavelet theory in image compression and design of remote sensing data compression system},
	year = {1998},
	note = {H007114},
	school = {Chongqing University (People{\textquoteright}s Republic of China)},
	type = {phdD.{Eng}.},
	address = {Ann Arbor},
	abstract = {Along with multimedia technology and compunication technology quickly development, data compression technology become key that this technology can or can{\textquoteright}t be application in business filed. In this background this paper stress study that several wavelet problems relational to image compression and how to quickly implement remote sensing data compress lossy in high compression rate combine with our item. So our work made up of two part. One part is about lossy; Another be about lossless. When image data be transformed with wavelet transform, signal which be not transformed should be equal to signal which be transformed in theory. But wavelet transform be as an filter banks, so we hope that it{\textquoteright}s distortion is more less in the view of signal reconstruct. Thogonal wavelet such as Daubechies{\textquoteright}s wavelet, it be not symmetry, but symmetry predicate linear phase. In order to signal construction, we must split the difference with reason between thogonal and linear phase. This problem can be resolved with an algorithm that we put forward a method of biorthogonal wavelet bases construction. We give the reconstruction method and the full conditions of biorthogonal wavelet bases with high regularity. The merits of it are that the filters of both analysis and synthesis parts can be optimized so that they can approximate to ideal low pass and high pass filters and the filter banks can derive biorthogonal wavelet bases with high regularity. Owing to wavelet transform having nature-layered characteristic, we demonstrates layer coding algorithm to sequence image in wavelet transform in this paper. Zhang Y Q. first published an article about MRME video coding in 1992. Xue give a MRME and lattic vector quantization hybrid coding schemes with improved decoded image quality. The drawback is computing complexity increasing evidently. In this paper, We propose a video coding algorithm-based intraframe and interframe combined prediction of wavelet coefficients. This algorithm efficiently removed spatial redundancy exploiting correlation between scale at some direction through intrafram tree\_constructed prediction of zero coefficient blocks. Temporal redundancy is made further reducing exploiting interfram MRME. Simulation result show that the computing complexity of our algorithm is far less than baseline MRME algorithm. The coding performance is equating with improved vector quantization MRME algorithm that has far large computing amount. We propose a new bit allocation algorithm relative to wavelet transform. It{\textquoteright}s ideal being differ from existing bit allocation algorithm so it{\textquoteright}s innovation. The algorithm capable of efficiently allocates a given qutoa of bit to an arbitrary set of different quantizers. This algorithm produces an optimal or very nearly optimal allocation, while allowing the set of admissible bit allocation values to be constrained to nonnegative integers. It is particularly efficiency to image coding based on wavelet for using this algorithm. Furthermore, the algorithm can be applied in schemes of needing bit allocation or vary bit coding. A lossless compression-coding algorithm is given in this paper, using it compression lossless dada. With our country new type weapon and spaceflight technological quickly growing, much data need be remote sensing and transmission. It is immediacy relation to new type weapon test. In order to ample demand, two means can be carried into execution. One way being wash out equipment in being, another being change equipment inbeing in order to satisfy our request, so much money can be saved. The ideal can be come true using our technology. Basis characteristic of data and require of processing, a coding algorithm of high dynamic dictionary plus model be given. The performance of the algorithm (such compression rate, compression time) is increased notability compare to existing algorithm. Expert identified this system. Key word: Wavelet transform Image Compression Filter Banks Remote Sensing Data Compression Dictionary Coding},
	keywords = {(UMI)AAIH007114, 0489:Information Technology, Applied sciences, Information Technology},
	url = {http://search.proquest.com/docview/1027143776?accountid=14270},
	author = {Li, Bo}
}
