臺灣博碩士論文加值系統

JPEG壓縮已經被大家廣泛的利用，但壓縮後的影像卻無法保有原先影像的品質，常常會使得影像的解析度變差。為了要讓壓縮過後的影像能夠保有良好的品質，在本篇論文中提出一個方法來恢復原有的影像品質。首先先探討什麼資料會在壓縮過程中遺失，接著基於人眼視覺的特性，把遺失的資料補回到JPEG影像中，希望能夠得到和原先未壓縮的影像類似的結果。
參照JPEG的壓縮格式，先將影像切成許多8*8區塊(8*8 Block)。觀察後發現，影像邊緣處(Edge)在壓縮過後會損失較多資料。因此，針對這些包含邊緣的區塊(Edge Block)加以處理以期增加影像的品質。首先，我們定義一組邊緣的標準模型，根據這個模型將影像中包含邊緣的區塊分成許多不同的類別；此外，將定義模型中，所有包含邊緣的區塊在壓縮過後會遺失的離散餘弦轉換係數(DCT Coefficients)資料找出來並存放在於資料庫中。最後根據分類好的邊緣區塊資訊，從資料庫中找到相對應的係數並加到JPEG影像中，即可得到較好品質的影像。實驗結果顯示，我們提出的方法能夠提升JPEG影像的品質，也能將壓縮過後產生的模糊加以銳利化。

JPEG is one of the most popular formats which are designed to reduce the bandwidth and memory space. A lossy compression algorithm is used in JPEG format, meaning that some information is lost and cannot be restored after compression. When high compression ratio is considered, certain artifacts are inevitable as a result of the degradation of image quality.
In this thesis, an image enhancement algorithm is proposed to reduce artifacts which are caused by JPEG compression standard. We found that severe degradation mostly occurs in the area containing edges. The degradation is resulted from the quantization step where high frequency components are eliminated. In order to compensate this kind of information loss, the proposed edge block detection method is performed to extract out edge blocks and categorize those edge blocks into several types of edge models in DCT (Discrete Cosine Transform) domain. Then, according the type of edge model, the pre-defined DCT coefficients are added back to the edge block. It is demonstrated by the experimental results that the proposed method successfully provides better performance in terms of sharpness while comparing to JPEG.

誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Introduction 1
1.2 Organization of the Thesis 2
Chapter 2 Related Work 4
2.1 JPEG System Overview 4
2.1.1 Color Space Transform 5
2.1.2 Discrete Cosine Transform 6
2.1.3 Quantization 7
2.2 Related Works of Edge Detection 8
2.2.1 Edge Detection 8
2.2.2 Edge Block Detection 10
2.2.3 Edge Block classification 11
2.3 Bilateral Filter 12
2.4 Image Quality Measurement 14
2.4.1 PSNR 14
2.4.2 Sharpness Measurement 15
Chapter 3 Image Enhancement with Recovering DCT Coefficients 17
3.1 System Overview 17
3.2 Lost Data Observation 18
3.3 Edge Block Detection 21
3.4 Edge Block Classification 23
3.5 Image Enhancement 25
3.6 The Overhead Size 30
Chapter 4 Experimental Results 31
4.1 Simple images 31
4.2 Real images 34
Chapter 5 Conclusion and Future Work 42
5.1 Conclusions 42
5.2 Future Work 43
REFERENCE 44

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