多功能視訊編碼(H.266/VVC)即將成為最新一代之視訊編碼國際標準，能夠對超高解析度視訊進行高效率壓縮。H.266/VVC以四元樹與巢狀多元樹為編碼架構，針對四元樹進行巢狀的二元樹以及三元樹的多樣切割，也新增許多先進編碼工具，相較於前一代的高效率視訊編碼標準(HEVC/H.265)節省約40%的資料量，卻也增加相當多的編碼時間。本論文針對H.266/VVC的畫面內編碼，利用人眼視覺之最小可辨差異模型，計算出編碼單位內之視覺可辨差異像素的水平方向與垂直方向投影個數，利用隨機森林機器學習快速決策巢狀多元樹切割。實驗結果顯示本論文所提方法可有效加速H.266/VVC的畫面內編碼，同時維持良好的位元率與視訊品質。

Versatile Video Coding(H.266/VVC) will be the newest international video coding standard to effectively encode the ultra-high-definition video. The quad-tree with nested multi-type-tree(QT-MTT) structure in H.266/VVC provides various sizes of coding tree partitioning and allows the nested binary-tree (BT) split and ternary-tree (TT) split at each QT level. Furthermore, numerous advanced coding tools are equipped in the H.266/VVC encoder. Compared to High Efficiency Video Coding(HEVC/H.265), H.266/VVC reduces the data amount around 40%. However, the encoding time increases tremendously. For intra coding of H.266/VVC, this thesis utilizes the human vision model of Just Noticeable Difference(JND) to calculate the horizontal and vertical projections of visual difference pixels of JND within the coding unit. Fast MTT decisions are determined by random forest models of machine learning. Experimental results demonstrate that the proposed methods can effectively accelerate the encoding of H.266/VVC intra coding and maintain good bit-rate and video quality.

摘要 1
ABSTRACT 3
目錄 5
表目錄 7
圖目錄 9
第一章 緒論 13
1.1 多功能視訊編碼標準(H.266/VVC)介紹 13
1.1.1 預測架構 14
1.1.2 編碼單位(Coding Unit, CU) 15
1.1.3 畫面內預測(Intra Prediction) 19
1.2 研究動機與目的 23
1.3 論文架構 24
第二章 H.266/VVC畫面內編碼快速演算法之相關文獻回顧 25
2.1 H.266/VVC QT-MTT之相關快速演算法 25
2.2 QT-BT之相關快速演算法 31
第三章 所提出的畫面內編碼快速演算法 37
3.1 最小視覺可辨差異(JUST NOTICEABLE DIFFERENCE，JND) 37
3.2 隨機森林(RANDOM FOREST)介紹 42
3.3 MTT決策演算法 47
第四章 實驗結果 53
4.1 H.266/VVC測試影像序列 53
4.2 實驗參數設定 55
4.3 結果與討論 56
第五章 結論與未來展望 73
參考文獻 75

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