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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58827完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 丁建均(Jian-Jiun Ding) | |
| dc.contributor.author | Chun-Hung Lin | en |
| dc.contributor.author | 林俊宏 | zh_TW |
| dc.date.accessioned | 2021-06-16T08:33:18Z | - |
| dc.date.available | 2020-07-17 | |
| dc.date.copyright | 2020-07-17 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-07-13 | |
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Krähenbühl, 'Compressed video action recognition,' in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 6026-6035. [43] C.-Y. Wu, N. Singhal, and P. Krahenbuhl, 'Video compression through image interpolation,' in Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 416-431. [44] O. Rippel, S. Nair, C. Lew, S. Branson, A. G. Anderson, and L. Bourdev, 'Learned video compression,' in Proceedings of the IEEE International Conference on Computer Vision, 2019, pp. 3454-3463. [45] S. Ma, X. Zhang, C. Jia, Z. Zhao, S. Wang, and S. Wanga, 'Image and video compression with neural networks: A review,' IEEE Transactions on Circuits and Systems for Video Technology, 2019. [46] D. Liu, Y. Li, J. Lin, H. Li, and F. Wu, 'Deep Learning-Based Video Coding: A Review and A Case Study,' arXiv preprint arXiv:1904.12462, 2019. [47] S. Huo, D. Liu, F. Wu, and H. Li, 'Convolutional neural network-based motion compensation refinement for video coding,' in 2018 IEEE International Symposium on Circuits and Systems (ISCAS), 2018: IEEE, pp. 1-4. [48] Z. Chen, T. He, X. Jin, and F. Wu, 'Learning for Video Compression,' IEEE Transactions on Circuits and Systems for Video Technology, pp. 1-1, 2019, doi: 10.1109/TCSVT.2019.2892608. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/58827 | - |
| dc.description.abstract | 根據Sandvine公司於2020年進行的調查，全球移動下載流量的65％為視訊。隨著物聯網和5G時代的來臨，高效能的視訊編解碼器對於節省存儲空間和頻帶使用而言，扮演越來越重要的角色。 對於多媒體資料壓縮而言，視訊資料可以達到最高的壓縮率，主要是利用時間上相鄰幀之間的相關性，該技術稱為「幀間預測」。對於預測圖像而言，僅將運動向量和殘差圖像編碼以降低位元速率。然而，在視訊壓縮中，幀間預測也是高計算複雜度的主要原因。 在此論文中，我們提出了透過觀察移動向量的變化來調整搜索區域的演算法，在不影響重建視訊品質和位元速率的前提下，我們試著節省比對次數。由實驗結果發現，我們提出的演算法特別適用於小動作影片或是有特定方向相機運動的影片。 此外，我們也研究了將移動向量編碼的方法。與傳統基於查表方式的編碼方法相比，我們提出了自適性算術編碼，進一步減少位元數。 總結來說，這篇論文回顧了視訊壓縮技術，並且對於移動向量提出搜索和編碼的相關演算法，做實驗驗證想法並討論其提升效能的可能性。 | zh_TW |
| dc.description.abstract | According to the survey from Sandvine in 2020, 65% of the downloaded data are video files. As 5G networks are deployed, efficient video codecs are essential for saving the requirements of storage and internet bandwidth. Among all kinds of multimedia, video can achieve the highest compression ratio due to the high correlation between adjacent frames. The technique of “Inter prediction” makes good use of interframe redundancy. For a predicted frame, only the motion vector and the residual image are coded to bitstream. However, in video compression, inter prediction usually leads to high computational complexity so we believe that there is still room for improvement. In this thesis, we propose an algorithm for adjusting the search window by observing the change of global motion vectors. We minimize the computation without affecting the quality of the reconstructed video and the bitrate. The proposed algorithm is especially suitable for static and fix camera slow movement. Besides, we investigate techniques to encode motion vector data into bitstream. Compared to the classic table-based coding method, we propose a context-based adaptive arithmetic coding (AAC) to further reduce the bitlength. All in all, we review the techniques in video compression and propose efficient methods for searching and coding motion vector. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T08:33:18Z (GMT). No. of bitstreams: 1 U0001-0907202016240300.pdf: 4208058 bytes, checksum: f420ea102e025d7a3d06f35046d917e7 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | 誌謝 i 中文摘要 ii ABSTRACT iii CONTENTS iv LIST OF FIGURES viii LIST OF TABLES xii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Fundamental tradeoff in video coding 2 1.3 Contribution of the thesis 3 1.4 Thesis Organization 4 Chapter 2 Video codec overview 5 2.1 H.264/Advanced Video Coding (AVC) 7 2.2 H.265/High Efficiency Video Coding (HEVC) 8 2.3 AV1 10 Chapter 3 Video Compression Technique 12 3.1 Picture Partitioning 12 3.1.1 Coding Tree Unit (CTU) 13 3.1.2 From Coding Tree Unit (CTU) to Coding Unit (CU) 14 3.1.3 From Coding Unit (CU) to Prediction Unit (PU) 16 3.1.4 From Coding Unit (CU) to Transform Unit (TU) 16 3.1.5 Downsampling 4:2:0 17 3.1.6 Shape-adaptive 18 3.1.7 Summary 19 3.2 Intra Coding 19 3.2.1 Multiple directions supported 20 3.2.2 Most Probable Modes (MPMs) in Luma Block 22 3.2.3 Chroma from Luma 23 3.2.4 Summary 23 3.3 Inter Coding 24 3.3.1 Three types of frames: I, B, P 24 3.3.2 Motion Estimation – Motion Vector Representation 25 3.3.3 Motion Estimation – Block Matching Algorithm 27 3.3.4 Motion Compensation 29 3.4 RD cost optimization 30 3.5 Transform and Quantization 31 3.5.1 Karhunen-Loeve Transform (KLT) 32 3.5.2 Discrete Cosine Transform (DCT) 33 3.5.3 Quantization 34 3.6 Loop Filtering 34 3.6.1 Deblocking Filter (DBLK) 36 3.6.2 Sample Adaptive Offset (SAO) 36 3.6.3 Summary 39 3.7 Entropy Coding 40 3.7.1 Shannon’s source coding theorem 41 3.7.2 Context-based Adaptive Variable Length Coding (CAVLC) 42 3.7.3 Context-based Adaptive Binary Arithmetic Coding (CABAC) 44 3.7.4 Summary 45 Chapter 4 Selection of matching function 46 4.1 The problem -- Error matching 46 4.2 Experiment – Matching criterion 49 4.3 Result 51 4.4 Summary 51 Chapter 5 Proposed adaptive search range adjustment algorithm 53 5.1 Review: Block-based motion estimation 53 5.2 Related work 54 5.3 Proposed search range adjustment based on camera movement 55 5.4 Experiment 56 5.4.1 Dataset 56 5.4.2 Tweaking parameters 57 5.4.3 Evaluation 61 5.5 Result 62 5.6 Summary 63 Chapter 6 Proposed context-based adaptive arithmetic coding for motion vector difference 64 6.1 Related work 65 6.2 Proposed adaptive arithmetic coding (AAC) 66 6.3 Experiment 68 6.3.1 Zigzag scanning through video 68 6.3.2 Context modeling (CM) 70 6.3.3 Multiple context modeling by zero percentage 73 6.3.4 Reference distance (current, past1, past2) 77 6.3.5 Initial frequency table 80 6.3.6 Update frequency table 82 6.4 Result 84 6.5 Summary 87 Chapter 7 Conclusion and future work 88 7.1 Conclusion 88 7.2 Future work 89 REFERENCE 90 | |
| dc.language.iso | en | |
| dc.title | 可適性移動向量搜尋和編碼演算法 | zh_TW |
| dc.title | Adaptive Motion Vector Searching and Encoding Algorithms for Video Compression | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 歐陽良昱(Liang-Yu Ou Yang),許文良(Wun-Liang Hsu),劉俊麟(Zun-Lin Liu) | |
| dc.subject.keyword | 資料壓縮,視訊壓縮,幀間預測,運動估計,運動補償,搜尋範圍調整,物件追蹤,熵編碼,算數編碼, | zh_TW |
| dc.subject.keyword | Data compression,Video compression,Inter-frame prediction,Motion estimation,Motion compensation,Search window adjustment,Object tracking,Entropy coding,Arithmetic coding, | en |
| dc.relation.page | 95 | |
| dc.identifier.doi | 10.6342/NTU202001414 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-07-13 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
| 顯示於系所單位： | 電信工程學研究所 | |
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