Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/24377
標題: 植基於離散小波轉換針對向量量化解壓縮影像品質改善之研究
A Study of Improving the Quality of VQ Decompressed Image Based on DWT
作者: 羅雅馨
Lo, Ya-Hsin
關鍵字: 差值矩陣
Difference matrix
差值編碼簿
離散小波轉換
影像壓縮
向量量化編碼
difference codebook
discrete wavelet transform
image compression
vector quantization
出版社: 資訊管理學系所
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摘要: 隨著科技的發達,越來越多的影像在網際網路上被流傳,因此有不少的壓縮技術被提出來處理這些影像。以傳統VQ (Vector Quantization)的方式對影像進行壓縮編碼,可以得到不錯的壓縮比,影像的還原品質也在人眼可接收的範圍內,但其壓縮後還原的影像品質無法有效提升。所以如何兼顧壓縮比和影像的還原品質,是一個重要的議題。此篇論文中,以VQ技術為基礎提出兩種影像壓縮方法。在方法一中,我們利用原始影像的區塊平均值將原始影像轉化成差值矩陣(Difference matrix),然後用若干影像之差值矩陣訓練一本差值編碼簿藉此來壓縮影像。此方法雖然可以利用更小的索引表還原出更好的影像品質,還得要額記住每個區塊像素值的平均數。但也由於影像特性,此一額外資訊可以壓縮為較小的資料檔。 第一個方法雖然得到好的影像品質,但也因為額外資訊所以降低了影像壓縮率。在第二個方法中,我們也得到了一本索引表和一個差值矩陣。主要先將原始影像進行DWT (discrete wavelet transform)轉換,然後以VQ方法壓縮DWT後的轉換影像,達到影像壓縮目的。藉由縮小索引表的總位元數,一方面也調整了差值矩陣的差值來改善影像品質和壓縮率。此外,並紀錄VQ解壓縮後之DWT轉換影像與原始DWT轉換影像的差值矩陣,做為解壓縮後影像品質的調整依據。藉由差值矩陣的誤差控制,可以將VQ壓縮法由失真壓縮調整到近乎無失真壓縮。實驗結果皆顯示,藉由索引表和差值矩陣的可調整性,影像品質和壓縮率得到明顯的改善。
Image distribution on the Internet is becoming frequent and common with the development of digital technology. So, there are many compression techniques to be proposed to process these images. The better compression rate can be achieved by traditional vector quantization (VQ) method, and the quality of the recovered image can also be accepted. But, the decompressed image quality can’t be promoted efficiently, so how to balance the image compression rate and the image recovering quality is an important issue. In this thesis, we propose two methods based on VQ scheme. Firstly, a new approach of VQ encoding is proposed to compress the image, we transform each image into a difference value’s matrix rather than a pixel value’s image before VQ compression. On the other words, we changed the subject of VQ scheme from an image to its corresponding difference value’s matrix. In this method, the better decompressed image quality can be achieved by a smaller index table, but some additional recovery data of pixel value from its difference value, should be recorded. However, according to the image characteristics, the recovery data can be compressed into a group of smaller data. Although the better image quality can be achieved by the first method, the image compression rate is decreased due to the additional information. By our second method, an index table and a difference matrix are also generated. In order to improve the image quality and image compression rate, any giving image is transformed by discrete wavelet transform (DWT) to generate its DWT image which will be compressed by VQ method further. The total number of bits used in the DWT image’s index table will be reduced, and the difference values of difference matrix will be adjusted. Besides, we compute the difference values between the original DWT image and the VQ decompressed DWT image to get their difference matrix which is the adjustable basis of the final decompressed image quality. By controlling the deviation of this difference matrix, the case of nearly lossless compression for VQ method can be achieved. Experimental results show that our method can improve the image quality and compression rate efficiently by properly tuning the deviation of difference matrix and the size of VQ codebook.
URI: http://hdl.handle.net/11455/24377
其他識別: U0005-1607201211584200
文章連結: http://www.airitilibrary.com/Publication/alDetailedMesh1?DocID=U0005-1607201211584200
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