成功大學博碩士論文系統

簡易檢索 / 詳目顯示

回結果列表

研究生：	蘇偉祺 Su, Wei-Chi
論文名稱：	利用多重解析度與紋理以達成動態偵測之解交錯器 Deinterlacing with Multiresolution-based Texture-adaptive Motion Detection
指導教授：	李國君 Lee, Gwo Giun
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 電機工程學系 Department of Electrical Engineering
論文出版年：	2006
畢業學年度：	94
語文別：	英文
論文頁數：	88
中文關鍵詞：	小波、多重解析度影像分析、動態偵測、紋理偵測、解交錯
外文關鍵詞：	deinterlacing, multiresolution image analysis, motion detection, texture detection, ELA, wavelet
相關次數：	點閱：128 下載：1
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

　　動態適應性的解交錯演算法利用所偵測到的動態資訊來選擇使用時間或是空間的補點方式。因此，正確的動態偵測是必要的。細微的紋理在小範圍內擁有很高的像素變化，時常造成錯誤的動態偵測。本論文主旨在於提出一個利用小波實現多重解析度影像分析，用紋理偵測找出細微紋理來提高正確的動態偵測並進一步產生更好的解交錯結果。論文中也提出了一個五個方向的邊緣補點法的變化型(5-tap ELA)，利用紋理的資訊來改善空間方面的補點方式。最後，還有一個遞迴式的三幅圖動態偵測法可以用少量的代價而得到近乎四幅圖動態偵測法的效果。此演算法的信號雜訊比(PSNR)與視覺效果均勝過一些傳統的解交錯演算法。
　　硬體方面，兩個資料處理單元使用六乘六的區塊式的處理來協調。離散小波轉換器把九乘九的區塊轉換成六成七的紋理資料區塊來提供解交錯運算器所需的資訊。兩個資料處理單元之間並建有內部的資料傳遞路徑以來降低匯流排的頻寬。硬體以54百萬赫茲運作並使用UMC.18 m的製程來進行合成。

　　Motion-adaptive de-interlacing algorithm selects from inter-field and intra-field interpolations according to motion. Correct determination of motion information is essential for this purpose. Fine textures, having high local pixel variation, tend to cause false detection of motion. This thesis proposed a texture detection mechanism utilizing multiresolution technique to improve the correctness of motion detection. A modification of 5-tap ELA algorithm is also proposed using the texture information to yield better spatial interpolation results, while the occlusion problem is improved by the inter-field interpolation selection. Finally, a memory-efficient recursive 3-field motion detection algorithm is developed to achieve close-to 4-field detection result but little overhead comparing with traditional 3-field detection. The proposed algorithm provides higher PSNR and better perceptual visual quality than other deinterlacing algorithms as shown by the experimental results.
　　Block-based process in hardware design allows the two main datapath cores to collaborate at 6x6 block level. The DWT core performs (inverse) discrete wavelet transform to 9x9 input blocks and produces 6x7 (one row overlap) texture indices for the corresponding block to be deinterlaced by the DEI core. Bus bandwidth and DRAM access is kept minimal by direct passing of texture indices between the two datapath cores. The hardware operates at 54 MHz and is synthesized with Artisan UMC .18 um cell library.

Chapter　1　Introduction---------------------------------------------------------------1
　1.1　Motivation/Background-----------------------------------------------------------1
　1.2　Organization　of　This　Thesis--------------------------------------------------3
Chapter　2　Deinterlacing　Algorithms--------------------------------------------------4
　2.1　Non-motion　Compensated　Deinterlacing------------------------------------------5
　2.1.1　Spatial　Deinterlacing　Algorithm---------------------------------------------6
　2.1.2　Temporal　Deinterlacing　Algorithm--------------------------------------------9
　2.1.3　Spatio-temporal　Deinterlacing　Algorithm------------------------------------10
　2.2　Motion　Adaptive　Deinterlacing　Algorithm-------------------------------------11
　2.2.1　Median-----------------------------------------------------------------------11
　2.2.2　Motion　Detection------------------------------------------------------------12
　2.2.3　Fading　Motion　Parameter----------------------------------------------------16
　2.2.4　Hybrid　Methods--------------------------------------------------------------16
　2.3　Motion　Compensated　Deinterlacing　Algorithm----------------------------------17
Chapter　3　Multiresolution　Image　Analysis　using　Discrete　Wavelet　Transform-----18
　3.1　Multiresolution　Image　Processing　–　Subband　Coding------------------------19
　3.2　Multiresolution　Expansion-----------------------------------------------------20
　3.2.1　Series　Expansion------------------------------------------------------------20
　3.2.2　Scaling　Functions-----------------------------------------------------------21
　3.2.3　Wavelet　Functions-----------------------------------------------------------23
　3.3　Discrete　and　Fast　Wavelet　Transform----------------------------------------25
　3.3.1　Wavelet　Series　Expansion---------------------------------------------------25
　3.3.2　Discrete　Wavelet　Transform-------------------------------------------------25
　3.3.3　Fast　Wavelet　Transform-----------------------------------------------------26
Chapter　4　Proposed　Texture　Detection　for　Motion　Adaptive　Deinterlacing--------28
　4.1　Wavelet-based　Texture　Detection----------------------------------------------29
　4.2　Recursive　3-field　Motion　Detection------------------------------------------32
　4.2.1　Recursive　3-field　Motion　Detection----------------------------------------32
　4.2.2　Inter-field　Interpolation　Selection----------------------------------------33
　4.3　Modified　5-tap　Edge-based　Line　Average-------------------------------------34
　4.4　Decision-making　of　the　Proposed　Motion　Detection--------------------------35
Chapter　5　Hardware　Architecture　Design　and　Implementation-----------------------37
　5.1　Application　Specification-----------------------------------------------------37
　5.2　Architecture　Design-----------------------------------------------------------37
　5.2.1　Module　Design---------------------------------------------------------------39
　5.2.2　Data　Flow-------------------------------------------------------------------47
　5.2.3　Input　and　Output　Bandwidth------------------------------------------------51
　5.2.4　Memory/Buffer　Configuration-------------------------------------------------54
Chapter　6　Verification　and　Experimental　Result-----------------------------------57
　6.1　Verification-------------------------------------------------------------------57
　6.1.1　Sub-module　Verification-----------------------------------------------------59
　6.1.2　System　Verification---------------------------------------------------------60
　6.2　Experimental　Result-----------------------------------------------------------60
　6.2.1　PSNR　Comparison　and　Subjective　Quality　Comparison-----------------------60
　6.2.2　Implementation　Result　and　Summary-----------------------------------------70
Chapter　7　Conclusions　and　Future　Work--------------------------------------------72
　7.1　Conclusions--------------------------------------------------------------------72
　7.2　Future　Work-------------------------------------------------------------------73
References
Appendix
                                    

[1]　　　The　Grand　Alliance.　“The　U.S.　HDTV　standard,”　IEEE　Spectrum,　vole.　32,　issue.　4,　pp.　36-45,　April　1995.

[2]　　　T.　Doyle　and　M.　Looymans,　“Progressive　scan　conversion　using　edge　information,”　Signal　Processing　of　HDTV　II,　pp.　711–721,　1990.

[3]　　　M.　H.　Lee,　J.　H.　Kim,　J.　S.　Lee,　K.　K.　Ryu　and　D.　I.　Song,　“A　new　algorithm　for　interlaced　to　progressive　based　on　directional　correlations　and　its　IC　design,”　IEEE　Transactions　on.　Consumer　Electronics,　vol.　40,　pp.　119-125,　May　1994.

[4]　　　C.　J.　Kuo,　C.　Liao　and　C.　C.　Lin,　“Adaptive　Interpolation　Technique　for　Scanning　Rate　Conversion”,　IEEE　Transactions　on　Circuits　and　Systems　for　Video　Technology,　vol.　6,　no.　3　pp.　317-　321,　June　1996.

[5]　　　H.　Y.　Lee,　J.　W.　Park,　T.　M.　Bae,　S.　U.　Choi　and　Y.　H.　Ha,　“Adaptive　scan　rate　up-conversion　system　based　on　human　visual　characteristics,”　IEEE　Transactions　on　Consumer　Electronics,　vol.　46,　issue.　4,　pp.　999–1006,　November　2000.

[6]　　　G.　L.　Li　and　M.　J.　Chen,　“High　performance　de-interlacing　algorithm　for　digital　television　displays,”　Journal　of　Display　Technology,　vol.　2,　issue.　1　pp.　85-90,　March　2006.

[7]　　　B.Bhatt,　F.　Templin,　B.　Hogstrom,　H.　Derovanessian,　S.　Lamadrid　and　J.　Mailhot,　Grand-alliance　HDTV　multi-format　scan　converter,”　IEEE　Transactions　on　Consumer　Electronics,　vol.　41,　pp.　1020-1031,　November　1995.

[8]　　　L.　Yu,　J.　Li,　Y.　Zhang　and　Y.　Shen,　“Motion　adaptive　deinterlacing　with　accurate　motion　detection　and　anti-aliasing　interpolation　filter,”　IEEE　Transaction　on　Consumer　Electronics,　vol.　52,　issue.　2,　pp.　712-717,　May　2006.

[9]　　　P.　D.　Filliman,　T.　J.　Christopher　and　R.　T.　Keen,　“Interlace　to　progressive　scan　converter　for　IDTV,”　IEEE　Transactions　on　Consumer　Electronics,　vol.　38,　pp.　135–144,　August　1992.

[10]　　　　J.　Salo,　Y.　Nuevo　and　V.　Hameenaho,　“Improving　TV　picture　quality　with　linear-median　type　operations,”　IEEE　Transactions　on　Consumer　Electronics,　vol.　34,　no.　3,　pp.　373–379,　August　1988.

[11]　　　J.　W.　Woods　and　S.-C.　Han,　“Hierarchical　motion　compensated　de-interlacing,”　Proc.　SPIE,　vol.　1605,　pp.　805–810,　1991.

[12]　　　F.　M.　Wang,　D.　Anastassiou　and　A.　N.　Netravali,　“Time-recursive　deinterlacing　for　IDTV　and　pyramid　coding,”　Signal　Processing:　Image　Communication,　vol.　2,　pp.　365–374,　1990.

[13]　　　G.　de　Haan　and　E.　B.　Bellers,　“De-interlacing　of　video　data,”　IEEE　Trans.　Consumer　Electronics,　vol.　43,　pp.　819–825,　Aug.　1997.

[14]　　　Y.　Yang,　Y.　Chen　and　W.　Zhang,　“Motion　adaptive　deinterlacing　combining　with　texture　detection　and　its　FPGA　implementation,”　Proceedings　of　2005　International　workshop　on　VLSI　Design　and　Video　Technology,　vol.　2,　pp.　316-319,　May　2005.

[15]　　　T.　S.　Chong,　O.　C.　Au,　W.　S.　Chau　and　T.　W.　Chan,　“A　content　adaptive　de-interlacing　algorithm,”　IEEE　International　Symposium　on　Circuit　and　Systems,　vol.　5,　pp.4923-4926,　May　2005.

[16]　　　G.　G.　Lee　and　C.　H.　Chen,　"A　multiresolution　wavelet　analysis　and　Gaussian　Markov　random　field　algorithm　for　breast　cancer　screening　of　digital　mammography,"　IEEE　Nuclear　Science　Symposium　&　Medical　Imaging　Conference,　vol.　3,　pp.　1737-1741,　June　1996.

[17]　　　T.　Chang　and　C.　C.　J.　Kuo,　"Texture　analysis　and　classification　with　tree-structure　wavelet　transform,"　IEEE　Transactions　on　Image　Processing,　vol.　2,　no.　4,　pp.　429-441,　October　1993.

[18] M.　Antonini,　M.　Barlaud,　P.Mathieu　and　I.　Daubechies,　“Image　coding　using　wavelet　transform,”　IEEE　Transaction　on　Image　Processing,　vol.　1,　no.　2,　pp.　205-220,　April　1992.

[19]　　　M.　Unser,　“Texture　classification　and　segmentation　using　wavelet　frames,”　IEEE　Transaction　on　Image　Processing,　vol.　4,　no.　11,　pp.1549-1560,　November,　1995.

[20]　　　S.Livens,　P.　Scheunders,　G.van　de　Wouwer　and　D.　VanDyck,　“Wavelets　for　texture　analysis,　an　overview,”　Sixth　International　Conference　on　Image　Processing　and　Its　Applications,　vol.　2,　pp.581-585,　July　1997.

[21]　　　G.　Fan　and　X.　Xia,　“Wavelet-based　texture　analysis　and　synthesis　using　hidden　Markov　models,”　IEEE　Transactions　on　Circuits　and　Systems,　vol.　50,　no.　1,　January　2003.

[22]　　　C.　H.　Chen　and　G.　G.　Lee,　“Image　segmentation　using　multiresolution　wavelet　analysis　and　expectation-maximization　(EM)　algorithm　for　digital　mammography,”　Nuclear　Science　Symposium,　vol.　3,　pp.　1737-1741,　November　1996.

[23]　　　I.　Claude　and　A.　Smolarz,　“A　new　textured　image　segmentation　algorithm　by　autoregressive　modelling　and　multiscale　block　classification,”　International　Conference　on　Image　Processing　and　Its　Application,　vol.　2,　pp.　586-590,　July　1997.

[24]　　　R.　Gonzalez　and　P.　C.　Wintz,　“Digital　Image　Processing,”　2/E,　Addison-Wesley　Publishing　Company,　Ch.　7,　2002.

[25]　　　K.　Andra,　C.　Chakrabarti,　and　T.　Acharya,　“A　VLSI　architecture　for　lifting-based　forward　and　inverse　wavelet　transform,”　IEEE　Transactions　on　Signal　Processing,　vol.　50,　pp.　966–977,　April　2002.

[26]　　　M.　El　Shobaki,　“Verification　of　embedded　real-time　systems　using　hardware/software　co-simulation,”　Proceedings　on　Euromicro　Conference,　vol.　1,　pp.　46-50,　August　1998.

[27]　　　S.　F.　Lin,　Y.　L.　Chang　and　L.　G.　Chen,　“Motion　adaptive　interpolation　with　horizontal　motion　detection　for　deinterlacing,”　IEEE　Transaction　on　Consumer　Electronics,　vol.　49,　no.　4,　pp.　1256-1265,　November　2003.

[28]　　　Y.　L.　Chang,　S.　F.　Lin　and　L.　G.　Chen,　“Extended　intelligent　edge-based　line　average　with　its　implementation　and　test　method,”　Proceedings　of　the　2004　International　symposium　on　Circuit　and　Systems,　vol.　2,　pp.　II-341-344,　May　2004.

校內：2007-08-29公開
校外：2007-08-29公開

簡易檢索 / 詳目顯示

相關論文