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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 盧奕璋(Yi-Chang Lu) | |
dc.contributor.author | Ying-Yu Tseng | en |
dc.contributor.author | 曾纓喻 | zh_TW |
dc.date.accessioned | 2021-07-11T14:40:05Z | - |
dc.date.available | 2022-02-21 | |
dc.date.copyright | 2017-02-21 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-01-20 | |
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Optical Society of America, 2013. [6] Dikpal Reddy, Ashok Veeraraghavan, and Rama Chellappa. P2c2: Programmable pixel compressive camera for high speed imaging. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 329–336. IEEE, 2011. [7] Jianbo Yang, Xin Yuan, Xuejun Liao, Patrick Llull, David J Brady, Guillermo Sapiro, and Lawrence Carin. Video compressive sensing using gaussian mixture models. IEEE Transactions on Image Processing, 23(11):4863–4878, 2014. [8] Patrick Llull, Xuejun Liao, Xin Yuan, Jianbo Yang, David Kittle, Lawrence Carin, Guillermo Sapiro, and David J Brady. Coded aperture compressive temporal imaging. Optics express, 21(9):10526–10545, 2013. [9] Antonin Chambolle and Thomas Pock. A first-order primal-dual algorithm for convex problems with applications to imaging. Journal of Mathematical Imaging and Vision, 40(1):120–145, 2011. [10] Soren Ragsdale, 2009. https://www.flickr.com/photos/sorenragsdale/with/ 3192314056/. 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[17] Joel A Tropp and Anna C Gilbert. Signal recovery from random measurements via orthogonal matching pursuit. IEEE Transactions on information theory, 53(12):4655–4666, 2007. [18] Michal Aharon, Michael Elad, and Alfred Bruckstein. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE TRANSACTIONS ON SIGNAL PROCESSING, 54(11):4311, 2006. [19] Todd K Moon. The expectation-maximization algorithm. IEEE Signal processing magazine, 13(6):47–60, 1996. [20] Felix Heide, Markus Steinberger, Yun-Ta Tsai, Mushfiqur Rouf, Dawid Pająk, Dikpal Reddy, Orazio Gallo, Jing Liu, Wolfgang Heidrich, Karen Egiazarian, et al. Flexisp: a flexible camera image processing framework. ACM Transactions on Graphics (TOG), 33(6):231, 2014. [21] Neal Parikh, Stephen P Boyd, et al. Proximal algorithms. Foundations and Trends in optimization, 1(3):127–239, 2014. [22] Kaiming He, Jian Sun, and Xiaoou Tang. Guided image filtering. In European conference on computer vision, pages 1–14. Springer, 2010. [23] Dengyu Liu, Jinwei Gu, Yasunobu Hitomi, Mohit Gupta, Tomoo Mitsunaga, and Shree K Nayar. Efficient space-time sampling with pixel-wise coded exposure for high-speed imaging. IEEE transactions on pattern analysis and machine intelligence, 36(2):248–260, 2014. [24] Renaud Péteri, Sándor Fazekas, and Mark J. Huiskes. DynTex : a Comprehensive Database of Dynamic Textures. Pattern Recognition Letters, doi:10.1016/j.patrec.2010.05.009. http://projects.cwi.nl/dyntex/. [25] Ron Rubinstein, Tomer Peleg, and Michael Elad. Analysis k-svd: a dictionarylearning algorithm for the analysis sparse model. IEEE Transactions on Signal Processing, 61(3):661–677, 2013. [26] Depeng Yang, Gregory Peterson, and Husheng Li. High performance reconfigurable computing for cholesky decomposition. In Proceedings of the Symposium on Application Accelerators in High Performance Computing (UIUC’09). Citeseer, 2009. [27] Henrique S Malvar, Li-wei He, and Ross Cutler. High-quality linear interpolation for demosaicing of bayer-patterned color images. In Acoustics, Speech, and Signal Processing, 2004. Proceedings.(ICASSP’04). IEEE International Conference on, volume 3, pages iii–485. IEEE, 2004. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/78028 | - |
dc.description.abstract | 視訊壓縮感知能有效的提高拍攝視訊的最高幀率。本論文將以8倍幀率的視訊壓縮感知為研究主軸,針對視訊壓縮感知重建速度與品質進行探討,加速視訊重建速度至每秒30幀以上供使用者即時預覽,並進一步將視訊壓縮感知推廣到彩色視訊,提出一套重建品質更好的彩色視訊壓縮感知流程。
首先我們測定現有的兩種先備字典視訊壓縮感知重建演算法,從中挑選出品質較好的高斯混合模型視訊重建演算法進行加速,嘗試以演算法、程式設計和平行程式設計的方式加速重建流程,然而重建流程的運算量龐大,資料相依性高,使得傳統運算系統在執行時硬體效率不佳,無法達到即時預覽的幀率。因此我們進一步設計多層平行化結構的硬體加速視訊重建,基礎單元為視訊重建子單元,在雙時鐘域下,以深度管線化與平行化等硬體設計技巧進行設計達到220倍的加速,以TSMC 90 nm製程實現,硬體尺寸為3.15 mm^2,運作頻率設計為400 MHZ,功耗為368.89 mW。視訊重建子單元還能在不增加資料傳輸頻寬下進一步平行化為最多25倍的子單元組。4倍平行的子單元組每秒能重建42幀300x400的視訊,供使用者即時預覽。 不同於其他研究者將彩色視訊壓縮感知簡化為三個獨立的壓縮感知問題,我們提出了一套彩色視訊壓縮感知流程。彩色視訊壓縮感知流程中,我們提出一個CCGVF-D3演算法交換各顏色通道的資訊,搭配先備字典有效的提昇重建的彩色視訊品質,對一般的視訊能有1~3.5 dB的峰值信噪比增益。 | zh_TW |
dc.description.abstract | Video Compressive Sensing can increase the highest frame rate of video streams efficiently. In this thesis, we set our target of video compressive sensing to 8 times the frame rate. Balancing on both time and quality, our design achieves the video reconstruction rate of 30 fps to enable instant user preview. Furthermore, we extend the video compressive sensing to color video compressive sensing and propose a new color video compressive sensing flow for better video qualities.
First, we test two currently available dictionary-base video compressive sensing algorithms. We choose to accelerate Gaussian-mixture-model-base video reconstruction algorithm since it provides better quality. However, the huge amount of computation and dependency of data degrade the utilization rate of the computing systems when implemented by software. Therefore, we propose a multilevel-parallel hardware design for video reconstruction acceleration. The basic module, video reconstruction subunit, is implemented subunit with a two-clock domain scheme as well as deep pipeline and parallel techniques. Using TSMC 90 nm technology, each subunit takes about 3.15 mm^2 in area, and its power consumption is 368.89 mW when operating at 400 MHz. The hardware can achieve 220 times speed up when compared to the original software version. Subunits can be further grouped into a super module of 25-parallel subunits group without increasing I/O bandwidth. The 4-parallel subunit group system can achieve 42 fps reconstruction rate required for instant user preview. Different from other researches which simplify the color video compressive sensing to a problem of 3 independent compressive sensing channels, we propose a new color video compressive sensing flow. In our flow, we propose a CCGVF-D3 algorithm exchanging information between channels. Accompanied with pre-learned dictionary, CCGVF-D3 algorithm can improve color video quality by 1~3.5 dB PSNR gain. | en |
dc.description.provenance | Made available in DSpace on 2021-07-11T14:40:05Z (GMT). No. of bitstreams: 1 ntu-106-R03943052-1.pdf: 21468009 bytes, checksum: 17c7cf18119c11c8670c00681ba2cced (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 口試委員會審定書i
誌謝ii 摘要iii Abstract iv 1 緒論1 1.1 互補金屬氧化物半導體影像感測器架構簡介. . . . . . . . . . . . . . 1 1.2 曝光編碼簡介. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 高幀率視訊重建方法簡介. . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 論文架構. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 背景知識9 2.1 視訊壓縮感知系統介紹. . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 資料驅動的訊號重建方法. . . . . . . . . . . . . . . . . . . . . . . . . 10 2.2.1 超完備字典介紹. . . . . . . . . . . . . . . . . . . . . . . . . . 11 2.2.2 高斯混合模型介紹. . . . . . . . . . . . . . . . . . . . . . . . 14 2.3 一階原始對偶演算法介紹. . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.1 常用先備知識與鄰近算子最佳化. . . . . . . . . . . . . . . . 20 2.4 引導影像濾波器. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 3 視訊壓縮感知重建演算法評測25 3.1 先備字典學習. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 重建演算法實現. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3 視訊重建演算法評測. . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.1 基於超完備字典的重建演算法評測. . . . . . . . . . . . . . . 32 3.3.2 基於高斯混合模型的重建演算法評測. . . . . . . . . . . . . . 37 3.4 總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4 演算法分析與軟體加速47 4.1 演算法分析與改良. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 4.2 軟體加速. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3 使用圖形處理器加速運算. . . . . . . . . . . . . . . . . . . . . . . . . 52 4.4 總結. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 5 視訊小單元重建之硬體架構設計55 5.1 硬體理念與整體架構. . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 視訊重建子單元硬體架構. . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3 各電路模組. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 5.3.1 硬體運作頻率. . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.3.2 整合型三角解算器. . . . . . . . . . . . . . . . . . . . . . . . 63 5.3.3 除頻器. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.3.4 資料輸入頻寬. . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5.3.5 (·) 計算器. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 5.3.6 SRAM A 模組. . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.3.7 μ 解算器. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 5.4 視訊重建子單元組. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 5.5 硬體實驗結果. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 5.6 軟硬體運算時間比較. . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6 彩色視訊壓縮感知83 6.1 重建視訊與曝光型樣相關性. . . . . . . . . . . . . . . . . . . . . . . 84 6.2 CCGVF-D3 演算法組成說明. . . . . . . . . . . . . . . . . . . . . . . 86 6.2.1 保真度項. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 6.2.2 規律化函數項. . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6.2.3 CCGVF-D3 演算法建構實驗. . . . . . . . . . . . . . . . . . . 89 6.3 彩色視訊壓縮感知. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 6.3.1 彩色視訊壓縮感知實驗. . . . . . . . . . . . . . . . . . . . . . 97 6.3.2 彩色視訊壓縮感知流程. . . . . . . . . . . . . . . . . . . . . . 99 7 結論與展望105 7.1 結論. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 7.2 展望. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 參考文獻107 | |
dc.language.iso | zh-TW | |
dc.title | 視訊壓縮感知演算法與硬體加速器設計 | zh_TW |
dc.title | Design of Algorithms and Hardware Accelerators for Video Compressive Sensing | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 丁建均(Jian-Jiun Ding),簡韶逸(Shao-Yi Chien),劉宗德(Tsung-Te Liu) | |
dc.subject.keyword | 視訊壓縮感知,硬體設計,一階原始對偶演算法, | zh_TW |
dc.subject.keyword | Video Compressive Sensing,Hardware Design,First Order Primal Dual, | en |
dc.relation.page | 110 | |
dc.identifier.doi | 10.6342/NTU201700157 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-01-20 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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