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 本論文以湖北省科技廳項目“智能運(yùn)輸系統(tǒng)視頻信號采集系統(tǒng)識別算法研究”為背景,針對圖像信號采集和實(shí)時傳輸中的圖像壓縮問題,基于圖像編碼理論、人工神經(jīng)網(wǎng)絡(luò)理論和小波變換理論對靜態(tài)圖像的有損壓縮技術(shù)進(jìn)行了較深入的分析、比較和研究,提出了靜態(tài)圖像的有損壓縮方案。
In the first part the wavelet analysis theory is systematically summarized in the perspective of signal analysis and digital signal processing . in succession , the excellent of the wavelet image coding techniques are introduced with the focus on the ebcot coding algorithm . at the last part , aiming at the properties of hyperspectral images , three compression schemes are presented : dct + ebcot , differential method + ebcot and predictive method + ebcot 文中首先從信號分析和數(shù)字信號處理的角度對小波分析理論進(jìn)行了系統(tǒng)的總結(jié),在此基礎(chǔ)上介紹了基于小波的圖像編碼技術(shù)的優(yōu)秀成果,重點(diǎn)介紹了基于小波的ebcot編碼算法,接著,針對高光譜圖像的特征,本文提出了以下三種壓縮方案: dct變換+ ebcot的壓縮方案、差值法+ ebcot的壓縮方案及預(yù)測法+ ebcot的壓縮方案,對三種壓縮方案進(jìn)行了軟件仿真,并將仿真結(jié)果與其它壓縮方案進(jìn)行了比較。
The finally proposed compression scheme takes more psychoacoustic effe cts into consideration , and takes the advantage of classified process , dynamic dictionary and sinusoidal modeling with perceptual gradient . both of its bit rate and speech quality are superior to some existing international coding schemes and standards . 5 最后提出的綜合編碼方案比較多地考慮了心理聲學(xué)因素,融合了分類處理、動態(tài)字典和感知梯度建模思想,在編碼位率和合成語音質(zhì)量上都比現(xiàn)有的一些國際編碼方法和標(biāo)準(zhǔn)要好。
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 and ii procedure 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 方案首先利用小波多分辨分析性質(zhì),對圖像進(jìn)行小波分解,對分解后各子圖的小波系數(shù)進(jìn)行了統(tǒng)計分析,針對各子圖的小波系數(shù)特點(diǎn),對不同的子圖分別采用不同的壓縮方法,低頻子圖采用基于神經(jīng)網(wǎng)絡(luò)的自適應(yīng)預(yù)測編碼,高頻子圖采用基于神經(jīng)網(wǎng)絡(luò)的矢量量化編碼,從而實(shí)現(xiàn)對圖像數(shù)據(jù)的壓縮處理。本論文第一章介紹了數(shù)字圖像壓縮處理的國內(nèi)外當(dāng)前的概況以及其技術(shù)標(biāo)準(zhǔn)和分類。在第二章,介紹了數(shù)字圖像的矢量量化技術(shù)的數(shù)學(xué)思想和過程,對lbg算法和基于sofm神經(jīng)網(wǎng)絡(luò)的矢量量化進(jìn)行了闡述、分析。
With different modeling methods and quantization techniques , the speech compression schemes discussed in this thesis include : the compression based on general matching pursuit sinusoidal modeling , the compression based on sinusoidal modeling with perceptual gradient , the compression based on dynamic dictionary matching pursuit , the compression scheme using classified dynamic dictionaries , and the integrated compression scheme that combines the sinusoidal modeling with perceptual gradient and the classified dynamic dictionaries 針對各種不同建模方法和參數(shù)量化技術(shù),本文探討了基于普通匹配跟蹤正弦建模的壓縮編碼、感知梯度正弦建模壓縮編碼、基于動態(tài)字典匹配跟蹤的壓縮編碼、分類動態(tài)字典壓縮編碼,以及結(jié)合感知梯度正弦建模和分類動態(tài)字典的綜合編碼方案。