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研究生:李豫暉
研究生(外文):Yu-Hwe Lee
論文名稱:用於視覺感知編碼及處理之內涵加權轉換
論文名稱(外文):Content-Weighted Transforms for Perceptual Visual Coding and Processing
指導教授:楊家輝楊家輝引用關係黃振發黃振發引用關係
指導教授(外文):Jar-Ferr YangJen-Fa Huang
學位類別:博士
校院名稱:國立成功大學
系所名稱:電機工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:108
中文關鍵詞:KLT轉換視覺感知加權轉換
外文關鍵詞:KLT transformationvisual perceptualweighting transformation
相關次數:
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本論文共提出兩項主題。首先,對向量量化提出簡化演算法則。其次,對於影像亂度及視覺判定方法提出改善的建議。
以位元映射(bitmap)結合奇異值分解的方法來強化向量量化。其中我們利用奇異值分解所俱有之能量集中的特性能更有效掌握向量量化的品質,而且利用位元映射不僅可以降低計算時間更有便利於硬體上的處理的雙重特性。
植基於上述的各項優點,我們提出結合位元映射及奇異值分解的方法應用在向量量化上。實驗證明;提出的方法減少約90%的計算量。
另外,對於重要的評估處理過之影像的品質標準(傳統上多採用SNR, PSNR)。我們提出適合於視覺感知的內涵加權轉換的觀念。伴隨著影像內涵的變化在一定程度上會影響人視覺上的感受,而所提之方法便是在探討目標物在其位置上與其所屬區域的相對關係,並藉以作加權轉換處理,以提供適合人體視覺感知的影像評估架構。實驗證明;提出的方法在影像亂度評估及對處過後影像品質的評量較傳統的方法更適合於視覺感知。
In this thesis, it contains two major topics involving transform-based image compression and image analysis. Firstly, the reduction of computational complexity for vector quantization (VQ) of the singular value decomposition (SVD) transformed domain is proposed. Secondly, the perceptual activity measure and the visual perceptual assessment are suggested.
The fast search algorithm of the SVD transformed VQ, which combines the singular value decomposition (SVD) and bitmap search algorithms for vector quantization (VQ) are addressed. Firstly, we exploit the property of energy concentration of the SVD transformation to improve the quality of VQ techniques. To avoid the heavy computation related to SVD computation, we utilize the dual properties of bitmap, which have the advantages of more suitable in VLSI implementation, to reduce the computational complexity. Based on bitmap search schemes, the proposed fast bitmap SVD VQ can save more than 90% computation of the ordinary transformed VQ.
To develop an important quality assessment tool for compressed or processed images, the concept of content-based weighting transformation is proposed to achieve a better visual perceptual measure than traditional signal to noise ratio (SNR) or peak SNR (PSNR). Since the human visual perception to the target signal is highly dependent on its surroundings, the proposed method explores the relationship between the target object and the surrounding pixels by using weighted transforms to match with human visual perception. Simulation results show that the measures performed in the proposed content-based weighted transformation can better predict the human visual perception than traditional variance-based measures obtained in the ordinary domain for activity assessment and the performance assessment of compressed and processed images.
1. Introduction………………………………………………………..1
1.1 Modified Bitmap Search with the SVD-VQ (MBS-SVD-VQ)……………..1
1.2 Perceptual Activity Measures………………………………………………3
1.3 Digital Image Watermarking by Using New Activity Assessment On
The DCT Domain…………..………………………………………………5
1.4 An Objective Measure for Predicting Subjective Quality of Image……..….6
2. Vector Quantization Using Bitmap Search Algorithms………….9
2.1 Linearly Transformed VQ Techniques………………………………………9
2.1.1 SVD-VQ…………………………………………………………….11
2.2 Bitmap Search Algorithms……………………………….………………….13
2.2.1 Successive Bitmap Search (SBS)……………………………………13
2.2.2 SVD-VQ with the SBS Method (SBS-SVD-VQ)……………………16
2.2.3 Computational Complexity of SBS-SVD-VQ Method………………18
2.2.4 Modified Bitmap Search (MBS)……………………………………..20
2.2.4.1 Computational Complexity of MBS-SVD-VQ…………………23
2.3 Simulation Results and Discussions…………..………………………………24
2.4 Diminutive Conclusions…………………………………………………...….25
3. Perceptual Activity Measures Computed in Transformed Domains……………………………………………………….……30
3.1 Traditional Activity Measures………………………………………………..31
3.2 Perceptually Weighted Transforms…………………………………………...35
3.2.1 KL Transformation (KLT)……………………………………………36
3.2.2 Perceptual Transform Approximation………………………………..39
3.3 Perceptual Activity Measures of Image Blocks……….……………………...41
3.4 Experimental Results…………………………………………………………46
3.5 Diminutive Conclusions……………………………………………………...52
4. Digital Image Watermarking by Using DCT-Based Activity Assessment…………….…………………………………………….61
4.1 The Proposed Methods……………………………………………………….61
4.2 Perceptual Activity Measures of Image Blocks………………………………62
4.3 Experiments results…………………………………………………………..65
4.3.1 Embedding Processing…….…………………………………………65
4.3.2 Extraction Procedure…………………………………………………66
4.4 Diminutive Concludes………………………………………………….…….67
5. Content-Based Objective Measure for Predicting Subjective Visual Quality of Distorted Image…………………………………………70
5.1 Perceptually weighted transformation for the CSNR…………………………72
5.2 Simulation results and discussions……………………………………………76
5.2.1 Distortion by JPEG Compression……………………………………78
5.2.2 Distortion by JPEG2000 Compression………………………………81
5.2.3 Distortion by Lowpass Filtering……………………………………...82
5.2.4 Global Evaluation of All Distorted Images…………………………..83
5.3 Diminutive Concludes………………………………………...………………84
6. Conclusions……………………………………………………...…106
7. Reference……………..……………………………………………108
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