近年來由於網路和多媒體的盛行，影像的需求量越趨盛大，為了便於傳輸和儲存，許多影像壓縮規格被發展出。目前最受歡迎的影像壓縮國際標準JPEG有兩種壓縮模式，一種是無失真，另一是失真。為了達到高壓縮率，我們通常使用失真模式來壓縮一般照片和多媒體。然而，壓縮率愈高愈會產生像馬賽克的方塊，這種失真被稱為方塊效應。本論文主要探討如何消除JPEG壓縮影像的方塊效應。
消除方塊效應的方法有兩類:一類是以濾波消除雜訊的方式來消除方塊效應，另一類是在小波域進行消除方塊效應。Kuo方法是針對可見方塊進行濾波處理，Ramamurthi方法是把方塊進行分類再使用一維和二維的濾波器來處理，Shi方法是在小波域弱化水平與垂直的方塊邊緣。
本論文結合以上三種方法的優點，設計出一消除方塊效應的演算法。我們先以Shi的一階小波分解法消除方塊邊界，接著我們使用一維的濾波器來對影像進行輕度模糊，再利用Kuo方法將可見方塊的邊界濾除，最後使用Ramamurthi方法對剩餘方塊進行分類濾波。融合以上三種方法的優點，我們有效的消除方塊效應並能同時保留真正邊緣。未來我們可以在影像的銳化上做進一步的改善。

In recent years, the demand of images has been increasing because the Internet and multimedia prevails all over around. In order to transmit and store data, there are many compression standards. JPEG image compression standard is very popular nowadays which has two kinds of compression modes. One is lossless, the other is lossy. In order to get high compression rate, we often use the lossy mode to compress images and multimedia. However, the higher the compression rate, the more serious the blocks appear blocky. Degradation caused by this mosaic like artifact is called the blocking effect. We will focus on improving images with blocking effect after JPEG compression in this thesis.
There are two classes of deblocking methods: one is removing blocking effect as noises by filtering the compressed image, the other is removing blocking effect in the wavelet transform domain. The Kuo’s method only deals with visible blocks by filtering and the Ramamurthi’s method filters pixels of different classes by one-dimensional and two-dimensional filters. The Shi’s method weakens edge features in the wavelet domain.
We design a deblocking algorithm by combining the merits of the above three algorithms. At first we remove the block boundary by the one level Shi’s wavelet-based deblocking algorithm. Then we blur lightly the image by a lowpass filter in order to blur the block boundary. Then we filter the pixel at visible block boundaries but not inside blocks. Finally we filter pixels in the remaining blocks by the Ramamurthi’s method. By combining the merits of the three well known algorithms, we design an algorithm which eliminates the blocking edge and meanwhile maintains the edge information of images. In the future, we will focus on further improving the sharpness of the image.

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