本文選題：圖像哈希 + 多維尺度分析　； 參考：《廣西師范大學(xué)》2017年碩士論文

【摘要】：圖像哈希算法是數(shù)字媒體內(nèi)容安全研究領(lǐng)域的一個(gè)前沿課題。它可以將任意尺寸的圖像映射成一串短小的字符或者數(shù)字序列,現(xiàn)已廣泛應(yīng)用于圖像檢索、水印嵌入、圖像篡改檢測(cè)和圖像質(zhì)量評(píng)價(jià)等方面。在實(shí)際應(yīng)用中,圖像常常會(huì)受到一些正常的數(shù)字處理,如JPEG壓縮、亮度調(diào)整、伽瑪校正等,這些操作會(huì)改變圖像的數(shù)據(jù),但是并不改變圖像視覺(jué)內(nèi)容。因此,圖像哈希算法應(yīng)該將這些視覺(jué)內(nèi)容相同的圖像映射成相同或者相似的哈希序列,這是圖像哈希的第一個(gè)基本屬性,即感知魯棒性。圖像哈希的另一個(gè)基本屬性是唯一性,它要求視覺(jué)內(nèi)容不同的圖像應(yīng)該具有不同的哈希序列。顯然,感知魯棒性和唯一性是相互制約的兩個(gè)屬性。通常,感知魯棒性的提高會(huì)導(dǎo)致唯一性下降,反之感知魯棒性減弱則唯一性提高,兼顧兩者間的性能平衡是圖像哈希算法研究的一個(gè)重要指標(biāo)。本文利用多維尺度分析(Multidimensional Scaling,MDS)、對(duì)數(shù)極坐標(biāo)變換(Log-polar Transform,LPT)、邊緣檢測(cè)和多級(jí)小波分解等理論和技術(shù),研究圖像哈希算法,取得了兩項(xiàng)有意義的研究成果。第一項(xiàng)成果是基于多維尺度分析的魯棒圖像哈希算法,該算法能夠有效抵抗任意角度的旋轉(zhuǎn)并且具有較好的唯一性。第二項(xiàng)成果是基于邊緣檢測(cè)和小波統(tǒng)計(jì)特征的圖像哈希算法,該算法能夠較好兼顧魯棒性和唯一性,并且可應(yīng)用于半?yún)⒖紙D像質(zhì)量評(píng)價(jià)。論文具體研究成果如下。1.提出基于多維尺度分析的魯棒圖像哈希算法MDS是一種有效的數(shù)據(jù)分析技術(shù),已經(jīng)成功的運(yùn)用于數(shù)據(jù)可視化、目標(biāo)檢索、數(shù)據(jù)聚類等方面,但是,在圖像哈希方面的研究還很少。本文深入探討MDS理論,提出了一種聯(lián)合MDS和LPT的圖像哈希算法。該算法先用LPT和離散傅里葉變換(Discrete Fourier Transform,DFT)提取旋轉(zhuǎn)不變的特征矩陣,再通過(guò)MDS從特征矩陣中學(xué)習(xí)壓縮表達(dá),最終的哈希相似性判斷用相關(guān)系數(shù)來(lái)衡量。該算法能對(duì)抗任意角度的旋轉(zhuǎn)操作,這是因?yàn)長(zhǎng)PT將笛卡爾坐標(biāo)空間中的旋轉(zhuǎn)操作轉(zhuǎn)化成對(duì)數(shù)極坐標(biāo)空間中的平移操作,應(yīng)用DFT的平移不變性后即可得到旋轉(zhuǎn)不變特征,從理論上確保算法具有抗旋轉(zhuǎn)變換的能力。實(shí)驗(yàn)結(jié)果顯示,該算法對(duì)常見(jiàn)數(shù)字處理操作如斑點(diǎn)噪聲、椒鹽噪聲、縮放、對(duì)比度調(diào)整、亮度調(diào)整、高斯低通濾波、任意角度旋轉(zhuǎn)等穩(wěn)健,并且可以有效地區(qū)分視覺(jué)內(nèi)容不同的圖像。接收機(jī)操作特性(Receiver Operating Characteristics,ROC)曲線對(duì)比結(jié)果表明,該算法在魯棒性和唯一性方面的分類性能優(yōu)于多種文獻(xiàn)的哈希算法。2.提出基于邊緣檢測(cè)和小波統(tǒng)計(jì)特征的圖像哈希算法人眼是視覺(jué)系統(tǒng)的終端,研究圖像哈希在半?yún)⒖紙D像質(zhì)量評(píng)價(jià)應(yīng)用時(shí)應(yīng)該充分考慮人類的視覺(jué)特性。小波變換將圖像從空間域轉(zhuǎn)換到頻率域,不同頻帶反映了圖像的不同信息,低頻子帶是圖像的粗略表示,高頻子帶反映圖像的細(xì)節(jié)變化,主要側(cè)重于邊緣、輪廓和紋理,這與人類視覺(jué)系統(tǒng)(Human Vision System,HVS)感知圖像的多通道特性相似。為此,本文提出了一種基于邊緣檢測(cè)和小波統(tǒng)計(jì)特征的哈希算法。該算法先對(duì)輸入圖像進(jìn)行預(yù)處理得到規(guī)范化圖像,提取規(guī)范化圖像的邊緣信息,接著對(duì)邊緣信息進(jìn)行非重疊分塊,然后對(duì)每個(gè)圖像塊進(jìn)行多級(jí)小波分解,考慮到人眼對(duì)不同頻帶變化的敏感度不同,對(duì)不同頻帶賦予不同的權(quán)重系數(shù),最后對(duì)不同頻帶的小波統(tǒng)計(jì)特征進(jìn)行加權(quán)計(jì)算即可得到圖像哈希。哈希的相似性用歐氏距離來(lái)判斷。ROC曲線對(duì)比實(shí)驗(yàn)結(jié)果表明,該算法在魯棒性和唯一性方面的分類性能明顯優(yōu)于其他算法。討論了該算法在半?yún)⒖紙D像質(zhì)量評(píng)價(jià)應(yīng)用的性能,實(shí)驗(yàn)選取美國(guó)TEXAS大學(xué)圖像和視頻工程實(shí)驗(yàn)室提供的LIVE圖像庫(kù)為失真圖像測(cè)試集,通過(guò)非線性曲線擬合發(fā)現(xiàn),該算法的客觀評(píng)價(jià)值與LIVE圖像庫(kù)提供的主觀差異值(Different Mean Opinion Scores,DMOS)具有較好的相關(guān)關(guān)系。
[Abstract]:Image hash algorithm is a frontier topic in the field of content security research in digital media. It can map any size of image into a series of short characters or digital sequences. It has been widely used in image retrieval, watermark embedding, image tamper detection and image quality evaluation. In practical applications, images are often subject to Some normal digital processing, such as JPEG compression, brightness adjustment, gamma correction, etc., will change the image data, but do not change the image visual content. Therefore, the image Hashi algorithm should map the same visual content to the same or similar Hashi sequence, which is the first basic attribute of the image Hashi. Another basic attribute of image Hashi is uniqueness, which requires that images with different visual content should have different Hashi sequences. Obviously, the perceptual robustness and uniqueness are two attributes that are restricted to each other. Generally, the enhancement of perceptual robustness can lead to a uniqueness decline, and conversely, the robustness is weakened by the uniqueness of the perceptual robustness. Improving the performance balance between the two is an important index for the study of the image hash algorithm. In this paper, the Multidimensional Scaling (MDS), the logarithmic polar coordinate transformation (Log-polar Transform, LPT), edge detection and multistage wavelet decomposition are used to study the image hash algorithm, and two meaningful items have been obtained. The first achievement is a robust image hash algorithm based on multidimensional scale analysis. The algorithm can effectively resist arbitrary angle rotation and has better uniqueness. The second result is a hash algorithm based on edge detection and wavelet statistical features. The algorithm can give a good consideration to the robustness and uniqueness, and it can be well considered. It is applied to the semi reference image quality evaluation. The research results of this paper are as follows:.1. proposed that robust image hash algorithm based on multidimensional scale analysis (MDS) is an effective data analysis technique, which has been successfully applied to data visualization, target retrieval, data clustering and so on. However, the Research on image hash is very few. In the study of MDS theory, an image hash algorithm for combined MDS and LPT is proposed. The algorithm first uses LPT and discrete Fu Liye transform (Discrete Fourier Transform, DFT) to extract the invariant feature matrix, and then learning compression expression from the feature matrix through MDS, and the final hash similarity judgment is measured by the correlation coefficient. The algorithm can be used in this algorithm. This is because LPT transforms the rotation operation in the Descartes coordinate space into the translational operation in the logarithmic polar space. The rotation invariant features can be obtained after the translational invariance of DFT. It is theoretically ensured that the algorithm has the energy of anti rotation transformation. The experimental results show that the algorithm is used for the common digital place. The operation features such as speckle noise, salt and pepper noise, scaling, contrast adjustment, brightness adjustment, Gauss low pass filtering, arbitrary angle rotation and so on are robust, and can effectively distinguish images with different visual content. The receiver operating characteristics (Receiver Operating Characteristics, ROC) curve contrast results show that the algorithm is robust and unique. The performance of the surface classification is better than the Hashi algorithm of many kinds of literature.2.. The image Hashi algorithm based on edge detection and wavelet statistical features is the terminal of the visual system. The image Hashi should take full consideration of the human visual characteristics when the semi reference image quality evaluation is applied. The small wave changes the image from the space domain to the frequency domain. The different frequency bands reflect the different information of the image. The low frequency subband is a rough representation of the image. The high frequency subband reflects the details of the image, mainly focusing on the edge, contour and texture, which is similar to the multichannel specificity of the Human Vision System (HVS) perception image. This algorithm first preprocesses the input image to get the normalized image, extracts the edge information of the normalized image, then does not overlap the edge information, and then decomposes the multilevel wavelet into each image block, taking into account the different sensitivity of the human eye to the different frequency bands, and the different frequency bands are assigned to the different frequency bands. The similarity of hash hash in different frequency bands can be calculated with different weighting coefficients. The similarity of hash hash can be obtained by Euclidean distance. The results of.ROC curve comparison show that the performance of the algorithm is better than that of other algorithms in robustness and uniqueness. The algorithm is discussed in the semi reference graph. As for the performance of the application of quality evaluation, the experiment selected the LIVE image library provided by the image and video Engineering Laboratory of TEXAS University in the United States as a distorted image test set. The objective evaluation value of the algorithm was related to the subjective difference value (Different Mean Opinion Scores, DMOS) provided by the LIVE image library. Department.

【學(xué)位授予單位】：廣西師范大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP391.41

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本文編號(hào)：1773662