近乎於量化參數最佳化應用於HEVC畫面內解碼之後處理機制

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姓名

陳永祐(Yung-You Chen) 查詢紙本館藏

畢業系所

通訊工程學系在職專班

論文名稱

近乎於量化參數最佳化應用於HEVC畫面內解碼之後處理機制
(Nearly QP-Optimized Post Processing for HEVC Intra Prediction)

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至系統瀏覽論文 (2024-8-6以後開放)

摘要(中)

在影像品質不斷進步之下，人們對影像資料的需求量大幅增加。為了因應高解析度的影像，高效率視訊編碼(High Efficiency Video Coding，HEVC)能夠比上一代的視訊標準高出兩倍的壓縮率，這是因為高效率視訊編碼在影像壓縮技術中使用到編碼單元、預測單元、轉換單元以及量化等方式，而進行影像壓縮過程中，為了降低傳輸資訊，編碼使用到量化參數造成影像的失真。因此本論文解碼端使用卷積神經網路的架構進行反量化失真影像補償，而在此提出兩種卷積神經網路對於失真影像補償的主題，一個是CNN卷積神經網路對於各種影像品質優化的改善程度，另一個則是CNN卷積神經網路模型量化參數縮減。經過多次的實驗之後，在不影響原始影像的條件之下，解碼端透過CNN卷積神經網路模型只需要8個量化參數QP22,QP27,QP32,QP37,QP40,QP45,QP48,QP50取代原來QP31~QP51等31個量化參數且達到一樣的影像補償效果。

摘要(英)

With the continuous improvement of image quality, people′s demand for image data has increased significantly. In order to handle high-resolution images, High Efficiency Video Coding (HEVC) can be twice as compressed as the previous generation of video standards. Because of HEVC uses coding units、prediction units、transfor units and quantization in image compression technologies. In order to reduce transmission information, encoding uses quantization parameters(QP) to cause image distortion. Therefore,decoding uses the convolutional neural network(CNN) architecture to perform inverse quantization of distortion image compensation in the end of this paper, and here are two topics of CNN compensation for distorted images, one is CNN for various image quality of the optimization, another is the reduction of the quantization parameters number of the CNN model. After many experiments, without affecting the original image, the decoder only needs 8 quantization parameters QP22, QP27, QP32, QP37, QP40, QP45, QP48, and QP50 to cover the original CNN model 31 quantization parameters such as QP31~QP51, and achieve the same image compensation effect.
Keywords: HEVC, Intra prediction, CNN, Compensate for the distorted image, Reduction the number of CNN Models

關鍵字(中)

★ 高效率視訊編碼
★ 畫面內預測
★ 失真影像補償
★ 卷積神經網路
★ 卷積神積網路模型數量縮減

關鍵字(英)

★ HEVC
★ Intra prediction
★ Compensate for the distorted image
★ Reduction the number of CNN Models

論文目次

論文摘要 VII
Abstract VIII
誌謝 IX
章節目錄 X
附圖索引 XIII
附表索引 XVI
第一章、緒論 1
1.1高效率視訊編碼 1
1.2 HEVC編碼架構介紹 2
1.2.1編碼單元(Coding Unit) 3
1.2.2預測單元(Prediction Unit) 4
1.2.3轉換單元(Transform Unit) 5
1.2.4碼率失真代價函數(RD Cost) 6
1.2.5畫面內編碼預測 7
1.2.6 量化(Quantization) 12
1.3支持向量機介紹 13
1.4深度學習介紹 17
1.4.1人工神經網路 18
1.4.2倒傳遞神經網路 19
1.4.3深度神經網路(Deep Neural Network) 21
1.4.4卷積神經網路(Convolutional Neural Networks,CNN) 22
1.5研究動機與目的 25
1.6論文架構 26
第二章、相關文獻回顧 27
2.1 超分辨率技術(Super-Resolution, SR) 27
2.2 SVM應用於HEVC編碼單元(CU)快速深度決策演算法 29
2.3 CNN應用於HEVC以增進影像品質相關文獻 43
2.3.1 Study of A Deep Learning Architecture For HEVC Decoder 43
2.3.2 An In-loop Filter Based on Low-Complexit CNN Using Residuals in Intra Video Coding 46
2.3.3 Enhancing Hevc Compressed Videos With A Partition-Masked Convolutional Neural Network 49
第三章、探討個別最佳化CNN模型用於HEVC解碼端之後處理架構 58
3.1 整體系統架構 58
3.2 卷積神經網路訓練與測試 60
3.2.1 環境配置 60
3.2.2 資料前處理 61
3.2.3 模型訓練 61
3.3 性能探討與分析 63
3.3.1 性能探討 64
3.3.2 編解碼時間分析 74
3.4 結論分析 77
第四章、CNN模型樣本數量縮減 79
4.1 性能探討與比較 79
4.1.1性能探討 80
4.2 結論分析 85
第五章、結論與未來展望 86
參考文獻 87

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指導教授

林銀議(Yin-Yi Lin)

審核日期

2021-8-13

推文