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텍스쳐 기반 블록 분할을 이용한 새로운 프레임 보간 기법
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 선우명훈 | - |
| dc.contributor.author | 정호선 | - |
| dc.date.accessioned | 2018-11-08T08:09:20Z | - |
| dc.date.available | 2018-11-08T08:09:20Z | - |
| dc.date.issued | 2014-08 | - |
| dc.identifier.other | 17594 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/10890 | - |
| dc.description | 학위논문(석사)--아주대학교 일반대학원 :전자공학과,2014. 8 | - |
| dc.description.tableofcontents | Abstract I. Introduction 1 II. Proposed MCFI Algorithm 8 A. Texture-Based Wedgelet Partitioning 9 B. Modified Recursive Search 13 C. Partitioned Block Interpolation 15 III. Experimental Results 19 IV. Conclusion 25 Bibliography 26 | - |
| dc.language.iso | kor | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 텍스쳐 기반 블록 분할을 이용한 새로운 프레임 보간 기법 | - |
| dc.title.alternative | Jung Ho-Sun | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Jung Ho-Sun | - |
| dc.contributor.department | 일반대학원 전자공학과 | - |
| dc.date.awarded | 2014. 8 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 652803 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000017594 | - |
| dc.subject.keyword | Block matching algorithm (BMA) | - |
| dc.subject.keyword | distributed video coding (DVC) | - |
| dc.subject.keyword | frame interpolation | - |
| dc.subject.keyword | frame rate up-conversion (FRUC) | - |
| dc.subject.keyword | motion estimation (ME) | - |
| dc.description.alternativeAbstract | This thesis presents a novel motion compensated frame interpolation (MCFI) algorithm that includes texture-based wedgelet partitioning (TWP) and modified recursive search (MRS). TWP partitions a rectangular block into two wedge-shaped sub-blocks using the texture information, which makes a better approximation for an actual object region. Thus, detailed motions around the object’s boundaries can be more precisely represented than by existing MCFI algorithms. To reliably estimate the actual motion, the MRS algorithm is used in addition to TWP. MRS considers the distances between the predicted motion vectors and the candidate motion vectors, as well as the matching error. Experimental results reveal that the proposed MCFI can improve the average peak signal-to-noise ratio performance by up to 3.05 dB compared to existing MCFIs. On the average structural similarity metric, the proposed MCFI algorithm is superior to existing algorithms by a value of up to 0.0385. In addition, the proposed MCFI can reduce computational complexity by as much as 66.9% with respect to the sum of absolute difference compared with existing MCFIs. | - |
- Appears in Collections:
- Graduate School of Ajou University > Department of Electronic Engineering > 3. Theses(Master)
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