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研究生: NGUYEN QUYNH ANH
NGUYEN QUYNH ANH
論文名稱: 基於反半色調與灰階影像之影像壓縮
Image Compression based on Inverse Halftoning and Grayscale Image Colorization
指導教授: 楊傳凱
Chuan-Kai Yang
口試委員: 楊傳凱
Chuan-Kai Yang
林伯慎
Bor-Shen Lin
謝易錚
Yi-Zeng Hsieh
學位類別: 碩士
Master
系所名稱: 管理學院 - 資訊管理系
Department of Information Management
論文出版年: 2022
畢業學年度: 110
語文別: 英文
論文頁數: 63
外文關鍵詞: colorization, dithering image
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  • Further, to advance an algorithm in the data compression research field we are looking for a method related dithering image process. Nonetheless, whenever color images are dithered with binary dot patterns, it is exceedingly crucial to recover enough of the information that was originally present, and for this reason, we propose a method of using the halftoning algorithms that typically could not only store information in the image but also discard colors and fine details. One of the most important concepts here is to incorporate previously discarded data into the halftone patterns. Because of this, in addition to reproducing the image tone and maintaining the flatness of the image, halftone patterns also represent fine details and a small amount of color information. To this end, we use a quad-tree segmentation image as a reference to partially supply the color information that was lost in the original image. The quadtree image will serve as a guide to help the inverse grayscale image follow the true color information and coordinates to colorize the image and return it to the ground truth. The output shows the improvement of such storing the information under halftone pattern image, compression ratio, and the ability to reconstruct a compressed image in colorization techniques.

    Abstract 1 Acknowledgment 2 List of Figure 5 Chapter 1: Introduction 7 Chapter 2: Literature review 10 2.1 Digital Dithering 10 2.2 Halftoning image 11 2.3 Inverse Halftoning 12 2.7 Image Colorization 15 Chapter 3: Methodology 17 3.1 Compression Architecture 18 3.2 Decompression Architecture 25 3.2.1 Inverse grayscale image network 25 3.2.2 Colorization Network 28 Chapter 4: Experimental and Comparison 35 4.1 Experiment Parameter 35 4.2 Experiment Results 35 4.2.1 Compression Ratio 37 4.2.2 Evaluating reconstruction quality 43 4.3 Discussion work 47 4.4 User study 49 Chapter 5: Conclusion and Future work 51 APPENDIX 1: User Study Questionnaire 56 APPENDIX 2: Run Demo 61

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