以SEResNeXt運算法則應用於HEVC畫面間預測之後處理機制

簡易檢索 / 詳目顯示

回結果列表

研究生：	詹仁銘 JEN-Ming Chan
論文名稱：	以SEResNeXt運算法則應用於HEVC畫面間預測之後處理機制 Post Processing for HEVC Inter Prediction with SEResNeXt algorithm
指導教授：	林銀議 Yin-Yi Lin
口試委員:
學位類別：	碩士 Master
系所名稱：	資訊電機學院 - 通訊工程學系 Department of Communication Engineering
論文出版年：	2021
畢業學年度：	109
語文別：	中文
論文頁數：	109
中文關鍵詞：	畫面間預測、時間序列模型、影像後處理
相關次數：	點閱：15 下載：0
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

各項科技隨著時間蓬勃發展，現代人的生活與科技有密不可分的關係，而在多媒體方面也不例外，畫質不斷地提高、色彩也著墨不少，但是在這些高解析度影像背後，需要龐大的資料量，為了有效的壓縮高解析度影像的巨大資料量，HEVC(High Efficiency Video Coding)運用的許多方式有效的降低位元傳輸。
近期已經有許多研究將深度學習應用於HEVC後處理中，而這些研究的目的是要解決因為壓縮的過程中，在編碼端會造成影像的失真，本篇論文也是以增強影像畫質為目的，然而許多後處理方式仍然是著重在單張影像內的資訊，並沒有考慮到畫面與畫面之間其實有很大的相關性。在此篇論文中，我們提出SEResNeXt運算法的時間序列模型在HEVC解碼端增進影像品質，藉由觀察畫面之間的的特性以及利用時間序列的模型，在連續幀之間的相似性中提升模型性能。在我們結合整體架構後，最終在HEVC畫面間預測與參考程式HM16.0相比，可以達到BDBR減少7.084%，在BDPSNR增加0.244dB。

Technologies have flourished over time. Modern people’s live are closely related to technology, and multimedia is no exception. The resolution of pictures is much higher than before, but amount of data is required behind these. In order to effectively compress the huge amount of data, HEVC (High Efficiency Video Coding) are used to effectively reduce transmission bit.
Recently, there are many researches applying deep learning to HEVC post-processing, and the purpose of these researches is to solve the problem of image distortion caused by the encoding side during the compression process. This paper is also aimed at enhancing image quality. However, there are many post-processing algorithms which still focus on the information in a single image. Thus they fail to take the advantage of the inter-frame correlation in the video. In this paper, we propose the post-processing for HEVC inter prediction with SEResNeXt algorithm. By observing the characteristics between the images and using the time series model, the performance of the model is improved. After we combine the overall architecture, the experiment result of our algorithm compared with the reference program HM16.0 achieves up to 7.084% BDBR reduction and 0.244dB BDPSNR increase.

論文摘要    vii
Abstract    viii
章節目錄    x
圖目錄    xiii
表目錄    xvii
第一章、 緒論    1
1    高效率視頻編碼(HEVC)標準介紹    1
2    HEVC編碼架構介紹    2
2.1 HEVC架構    2
2.2 碼率失真函數(RD cost)    3
2.3 編碼單元(Coding Unit)    5
2.4 預測單元(Prediction Unit)    6
2.5轉換單元(Transform Unit)    7
2.6 量化參數(Quantization Parameter)    8
2.7 HEVC 編碼組態(Configuration)    9
3    支持向量機(Support Vector Machine)介紹    11
4    深度學習介紹    14
4.1 人工神經網路(Artificial Neural Networks)    15
4.2 深度神經網路(Deep Neural Network)    15
4.3 卷積神經網路(Convolutional Neural Networks, CNN)    18
5    研究動機與目的    20
6    論文架構    21
第二章、 先備知識與文獻回顧    22
1 畫面間預測(Inter Prediction)    22
1.1合併模式決策介紹(Merge Mode Decision)    22
1.2畫面間模式決策介紹(Inter Mode Decision)    25
2 SVM應用於HEVC畫面間編碼單元快速決策演算法    29
2.1 SVM編碼單元特徵選取    31
移動向量變異數(Motion Vector Variance)    32
Coded Block Flag (CBF)    35
鄰近編碼單元深度資訊 (Neighboring CU)    36
2.2 系統流程圖    38
2.3實驗數據    39
3 深度學習應用於HEVC畫面間解碼之後處理機制    40
3.1結合SVM及CNN應用於HEVC解碼端後處理    40
3.2消息理論背景    41
3.3 系統流程圖    43
3.4實驗數據    45
4  相關文獻    46
4.1 Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting    47
4.2    Aggregated Residual Transformations for Deep Neural Networks    52
4.3    Squeeze-and-Excitation Networks    53
第三章、 時間序列模型應用於HEVC解碼端後處理    55
1動機說明    55
2系統架構    55
3時間序列模型製作    57
3.1前處理階段(Pre-processing stage)    57
3.2訓練階段(Training stage)    58
3.3測試階段(Testing stage)    63
4空間域模型與時間域模型的並聯、串聯製作    65
4.1空間域模型與時間域模型的並聯應用    65
4.2空間域模型與時間域模型的串聯應用    66
第四章、 各架構實驗性能分析    68
1 實驗環境設置    68
2 Low-delay編碼結構下性能分析    69
2.1 並聯與串聯架構間數據分析    79
2.2 架構間圖片差異    81
2.3 碼率失真曲線說明    86
第五章、 結論與未來展望    89
參考文獻    90


                                

[1] I. E. G. Richardson, H.264 and MPEG-4 Video Compression: Video Coding for Next-generation Multimedia. Aberdeen, U.K.: John Wiley & Sons, 2003.
[2] “Generic coding of moving pictures and associated audio information,” ISO/IEC 13818-2: Video (MPEG-2), May 1996.
[3] “Coding of audio-visual objects - Part 2: Visual,” in ISO/IEC 14496-2 (MPEG-4 Visual Version 1), Apr. 1999.
[4] “Video coding for low bit rate communication, version 1,” ITU-T recommendation H.263, 1995.
[5] G. J. Sullivan, J.R. Ohm, W.J. Han and T. Wiegand,” Overview of the High Efficiency Video Coding (HEVC) Standard,” IEEE Trans. CSVT, vol. 22, no. 12, Dec. 2012.
[6] JCT-VC, “High efficiency video coding (HEVC) test model 15(HM15) encoder description,” JCTVC-Q1002, JCT-VC Meeting, Valencia, ES, Apr. 2014.
[7] P. Helle, S. Oudin, B. Bross, D. Marpe, M. O. Bici, K. Ugur, J. Jung, G. Clare, and T. Wiegand, “Block merging for quadtree-based partitioning in HEVC,” in Proc. IEEE Transactions on circuits and systems for video technology, vol. 22, no. 12, pp. 1720-1731, Dec. 2012.
[8] L. Zhao, X. Guo, S. Lei, S. Ma and D. Zhao, “Simplified AMVP for high efficiency video coding,” in Proc. IEEE ICIP, pp. 1-4, 27-30 Nov. 2012.
[9] J. L. Lin, Y. W. Chen, Y. W. Huang, and S. M. Lei, “Motion vector coding in the HEVC standard,” in Proc. IEEE Journal of Selected Topics in Signal Processing, vol. 7, no. 6, pp. 957-968, 3 July 2013.
[10] Y. Ismail and S. El-etriby, “Fast diamond search algorithm for real time video coding,” in Proc. IEEE ICNC, pp. 729-733, Feb. 2012.
[11] J.K. Liu, “Efficient HEVC inter prediction using SVM,” Department of Communication Engineering National Central University, Taiwan 32054, R.O.C., Jan 2019.
[12] C.K Hsieh, “CNN-Based Post-Processing for HEVC Inter Prediction,” Department of Communication Engineering National Central University, Taiwan 32054, R.O.C., July 2020.
[13] X. Shi, Z. Chen, H. Wang, D.Y. Yeung, W.K. Wong and W.C. Woo, “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting,” in NIPS, 2015.
[14] S. Xie, R. Girshick, P. Dollar, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in CVPR, 2017.
[15] J. Hu, L. Shen and G. Sun, “Squeeze-and-Excitation Networks,” in CVPR, 2018.

簡易檢索 / 詳目顯示

相關論文