本文關(guān)鍵詞： 遙感圖像 JPEG XR 張量分解 3D-SPECK 圖像壓縮　出處：《哈爾濱工業(yè)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：遙感圖像壓縮是信息與信號(hào)處理領(lǐng)域的一個(gè)重要的研究方向，隨著成像光譜技術(shù)的快速發(fā)展，圖像的存儲(chǔ)量也急劇增大，所以對(duì)遙感圖像進(jìn)行壓縮時(shí)很有必要的。現(xiàn)在存在很多種壓縮遙感圖像的方法，但是總體上來(lái)說(shuō)可以歸納為三類，第一類是基于變換的壓縮編碼算法；第二類是基于預(yù)測(cè)的壓縮編碼算法；第三類是基于VQ的壓縮編碼算法。在基于變換的遙感圖像壓縮編碼算法中，采用張量分解對(duì)高光譜圖像進(jìn)行變換是一種比較新穎的變換技術(shù)，這種技術(shù)不但能去除高光譜圖像空間上的相關(guān)性，還能去除譜間的相關(guān)性，然后結(jié)合3D-SPECK這種先進(jìn)的三維數(shù)據(jù)編碼方法，在不損失的圖像信息的情況下進(jìn)一步對(duì)圖像進(jìn)行了壓縮。 另外本文還研究了最近幾年新提出的壓縮算法JPEG XR標(biāo)準(zhǔn)中的熵編碼部分，它的壓縮性能接近JPEG2000，計(jì)算復(fù)雜度上卻要低得多。由于其具有計(jì)算復(fù)雜度低、內(nèi)存消耗少等特點(diǎn)，既支持無(wú)損壓縮，又支持有損壓縮，還支持區(qū)域解碼，HDR等等，不過(guò)壓縮時(shí)間還可以進(jìn)一步縮短，所以在FPGA上實(shí)現(xiàn)JPEG XR的熵編碼具有重要的應(yīng)用前景。 首先本文全面介紹了張量分解的原理和JPEG XR標(biāo)準(zhǔn)，重點(diǎn)分析了這兩種算法的優(yōu)缺點(diǎn)，然后提出了兩種遙感圖像壓縮方法，基于張量的高光譜圖像壓縮和基于JPEG XR的光學(xué)圖像壓縮，從軟件和硬件上對(duì)遙感圖像壓縮進(jìn)行了一個(gè)全面的分析。最后對(duì)兩種壓縮方法進(jìn)行了仿真實(shí)驗(yàn)，在基于張量的高光譜圖像編碼方法中，本文對(duì)比了3D-DWT等方法，信噪比提高了大約5dB左右；在基于JPEG XR的光學(xué)圖像壓縮部分，，本文對(duì)比了傳統(tǒng)的JPEG2000算法，在編碼和解碼時(shí)間，以及重建圖像的信噪比上進(jìn)行了對(duì)比，在同等的壓縮碼率下，JPEG XR的壓縮時(shí)間是JPEG2000的1/4。
[Abstract]:Remote sensing image compression is an important research direction in the field of information and signal processing. With the rapid development of imaging spectrum technology, the storage capacity of images increases dramatically. So it is necessary to compress remote sensing image. There are many methods to compress remote sensing image, but generally it can be classified into three categories: the first is compression coding algorithm based on transformation; The second kind is the compression coding algorithm based on prediction. The third is the compression coding algorithm based on VQ. In the transform based remote sensing image compression coding algorithm, Zhang Liang decomposition to transform hyperspectral image is a relatively novel transformation technology. This technique can not only remove the spatial correlation of hyperspectral images, but also remove the correlation between spectra, and then combine 3D-SPECK with the advanced 3D data coding method. The image is further compressed without loss of image information. In addition, this paper also studies the entropy coding part of the new compression algorithm JPEG XR proposed in recent years, and its compression performance is close to that of JPEG2000. Because of its low computational complexity and less memory consumption, it supports lossless compression, lossy compression, and region decoding HDR. However, the compression time can be further shortened, so the entropy coding of JPEG XR on FPGA has an important application prospect. Firstly, this paper introduces the principle of Zhang Liang decomposition and JPEG XR standard, analyzes the advantages and disadvantages of the two algorithms, and then proposes two remote sensing image compression methods. Hyperspectral image compression based on Zhang Liang and optical image compression based on JPEG XR. A comprehensive analysis of remote sensing image compression is carried out in software and hardware. Finally, two compression methods are simulated, which are based on Zhang Liang's hyperspectral image coding method. Compared with 3D-DWT and other methods, the signal-to-noise ratio (SNR) is improved by about 5 dB. In the part of optical image compression based on JPEG XR, the traditional JPEG2000 algorithm is compared, and the coding time and decoding time as well as the SNR of reconstructed image are compared. At the same compression rate, the compression time of JPEGXR is 1 / 4 of that of JPEG2000.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN911.73

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相關(guān)期刊論文 前1條

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