MPEG視訊編碼是目前最主要的動態影像壓縮標準系列，但影像經過DCT轉換再量化會產生失真及變形，於是我們提出對MPEG壓縮影像再做形態補償，即除了以DCT為主的編碼方式再加入布林濾波器。
布林運算經常使用在二階影像中，而我們以遮罩來代表布林函數表示式中的每一項minterm。但是若是要把遮罩運算使用在灰階影像上，則其在每個輸入視窗的遮罩運算將會變的很複雜。因此，我們將採用兩個遮罩來代表布林函數中的一個minterm，一個遮罩代表補數視窗；另一個則代表用來取出最大或最小值的視窗。為了更快速有效的編碼，我們所使用的布林濾波器將只有一個補數遮罩，也就是說只選用所有minterm的補數狀況都一樣的布林濾波器。
經過實驗結果的驗證，我們證實加入布林濾波器的還原影像的確比只有做位移補償的還原影像好，而所付出的代價就是增加編碼的複雜度及解碼必須做相對應的簡單遮罩運算。但真正最重要的是加入的解碼流程幾乎無損於播放的流暢，能真正做到即時播放。由於可看出我們非常成功的將布林濾波器加入MPEG編、解碼器。

MPEG is a primary standards for dynamic image compression. The error can be introduced in the quantization process of the block DCT transformation . In this sense, the shape compensation by Boolean filter is proposed by us to adjust the intensity distribution of the motion compensated image. That is, our dynamic images are coded by the kinds of Boolean filter in addition to motion vectors after DCT coding.
Boolean operation is usually applied to the binary images. The minterms of Boolean operations can be reprensented by masks. For the gray level images the mask operation is changed to be the more complex window operations. More clearly, there are two masks for a miniterm. One is a complement mask. The other is a maximum or minium window. For coding efficiency, in this paper a common complemented mask is used for two miniterms. In this sense, our Boolean filters are limited in a category of two miniterms with a same complemented structure.
Experiment results have demonstrated that images going through the extra Boolean filtering have better visual quality than images processed by motion compensation only. The price is paid by the coding complexity and a relatively simple decoding structure. However, it is the decoding process which is more crucial in real time play. Therefore, we successfully integrated the Boolean filtering into MPEG-1 system in software implementation.

目 錄

第一章 引言 1

第二章 MPEG動態影像壓縮原理 4
第一節MPEG簡介 4
第二節MPEG的視訊壓縮原理壓縮原理 5

第三章 布林代數與遮罩運算的關係 10
第一節 布林代數與布林濾波器 10
第二節 形態運算 11
3.2.1 遞增式 11
3.2.2 核心之基底 13
第三節 遮罩運算與布林濾波器 17
3.3.1 濾波器之遮罩運算 17
3.3.2 由形態濾波器到布林濾波器 17
3.3.3 加入補數視窗後的改變 19

第四章 最佳布林濾波器之選擇 21
第一節 絕對值差最佳化N點觀察濾波器 21
第二節 最佳化濾波器之決定 23
第三節 為一觀察視窗找出最佳布林濾波器 24
4.3.1 建立各種補數狀況的機率表 24
4.3.2 用機率表選擇最佳布林濾波器 25
4.3.3 實際例子說明 27
第四節 灰階遮罩運算 27
4.4.1 解決灰階問題之做法 29
4.4.2 灰階切片理論證明 34
第五節 減少候選布林濾波器之做法 36

第五章 系統實作 38
第一節 簡介 38
第二節 Berkely MPEG-1系統介紹 39
第三節 加入遮罩運算 41
第四節 MPEG stream之構造 43
第五節 解碼端流程 43

第六章 實驗結果與分析 45
第一節 連續灰階動態影像之形態補償 45
第二節 應用形態濾波器於Berkely-MPEG-1系統中 49
6.2.1 實驗環境說明 49
6.2.2 實驗參數配置 51
6.2.3 實驗結果顯示 55
第三節 結果討論 72

第七章 結論 74

參考文獻

[1] Rafael C.Gonzalez and Richard E.Woods, “Morphology”,Digital Image Processing, Addision-Wesley ,Inc.

[2] Jain,A.K,Fundamentals of Digital Image Processing, Englewood Cliffs,NJ: Prentice-Hall,1989
.
[3] Sperry R.H.and Parker K.J. “Segementation of speckle images based on levelcrossing statistics”J.Opt.Soc.AM.A/vol.8,No.3, pp.490-498, Mar. 1991

[4] Klinger J.W.,Vaughan,C.L.and Andrews L.T. “Segmentation of echocardiographic images using mathematical morphology”,IEEE Trans.on Biomedical engineering
,vol.35,NO.11, pp. 925-933,Nov. 1988

[5] Charles R.Giardina and Edward R.Doungerty, “Morphology Filters for Two-Valued Images”, Morphological Methods in Image and Signal Processing. Englewood Cliffs,NJ: Prentice Hall ,1989

[6] Charles R.Giardina and Edward R.Doungerty, “ Increasing ”, Morphological Methods in Image and Signal Processing, Englewood Cliffs,NJ: Prentice Hall ,1989

[7] Charles R.Giardina and Edward R.Doungerty, “ Appendix:Proposition A.39”, Morphological Methods in Image and Signal Processing, Englewood Cliffs,NJ: Prentice Hall ,1989

[8] Charles R.Giardina and Edward R.Doungerty, “ Basis for Kernel”, Morphological Methods in Image and Signal Processing, Englewood Cliffs,NJ: Prentice Hall ,1989

[9] E.R.Doughert and C.R.Giardina,”A Digital Version of the Matheron Representation theorem for Increasing in Terms of a Basis for the Kernerl”,IEEE Computer Vision and Pattern Recognition, pp. 534-536, 1986

[10]Edward R.Doungerty, “Optimal Mean-Squre N-Obsevation Digital Morphological Filters,I, Optimal Binary Filters”,in Computer Vision and Graph Image Processing,vol.55,No.1, pp. 36-54, Jan. 1992

[11]Charles R.Giardina and Edward R.Doungerty, “Umbra transform”, Morphological Methods in Image and Signal Processing, Englewood Cliffs,NJ: Prentice Hall ,1989

[12]Bede Liu and Andre' Zaccarin, "New Fast Algorithms for the Estimation of Block Motion Vectors," IEEE Trans. on Circuits and Systems for Video Technology,Vol.3,No.2,pp.148-157 ,Apr. 1993

[13]Ram Srinivasan and K. R. Rao, "Rredictive Coding Based on Efficient Motion Estimation," IEEE Trans. On Commun., Vol.COM-33,No.8,pp.888-896, Aug. 1985

[14]S.Kappagantula and K. R. Rao,"Motion Compensated Interframe Image Predicition," IEEE Trans. On Commun.,Vol.COM-33,No.9,pp. 1011-1015, Sep. 1985