Research On Lossy Compression Of Still Images

As the appearance of notion of information highway and digital globe and Internetworks develop widely,the transmission of information data increases rapidly,among these data,image information is the important means and source for people to acquire information and utilize information by its bigger quantity of data. Because many image information data need to be transmitted and preserved, it is difficult to meet the requests for image information data to be transmitted and preserved by depending only on increasing the width of signal band and the processing speed of computers, we need to use image compression techniques to help to satisfy the requests. In these days,image compression is put into practice widely in many fields such as biomedical applications,wireless communications,computer graphics etc.The paper is under the background of "The research on recognition algorithm of video signal sampling system in intelligent transportation system", which is a project of Hubei province scientific deparment. Pointed to the problem of image compression in image signal sampling and transmitting real time, this paper mainly discusses and analyzes lossy compression of still images deeply based on theories of image coding , artificial neural network , wavelet transform, the paper presents a compression scheme for still images. First the image is decomposed at different scales by using the wavelet transform, then the different quantization and coding schemes for each subimage are carried out according to its statistical properties and distributed properties of the coefficients. The wavelet coefficients in high frequency subimages are compressed and vector quantized based on neural network. The wavelet coefficients in low frequency bands, here adopts a scheme of image coding based on nonlinear predictor by using neural network, finally image is compressed.In the paper,Chapter 1 gives a comprehensive introduction of digital image compressing including its recent status , technical standards , classification in the world.Chapter 2 introduces briefly the thought andIIprocedure of vector quantization,describes LGB algorithm and vector quantization based on SOFM neural network.Chapter 3 discusses predictable coding in lossy and lossless aspects,analyzes adaptive predictable coding based on BP neural network,introduces the evaluation of algorithm on neural network in image compression.Chapter 4 discusses the applications of mathematical transformation in image compression and does experiments related, analyzes the strategies of image coding in transformed domain.In Chapter 5 images are decomposed and represented by wavelet transform, then discusses the characteristics and effects of wavelet functions in image compression,analyzes the wavelet coefficients after images are decomposed; Based on the theories and analyses in the prior chapters,the paper presents an image compression scheme and gives results.The test results shows that the image compression scheme is practical and helpful to map into the local content of images to get rid off redundancy,so that ,it can require satisfactory results of image compression.