H.266/VVC 被視為迎來全新時代的里程碑。其主要目標在於為使用者提供全方位的高品質編碼影像服務。相較於其前身H.265/HEVC，H.266/VVC 在相同的品質標準下展現出更優越的性能。具體而言，它呈現出更高的編碼壓縮率，可提升約30%至50% 的效能。這意味著使用H.266/VVC 進行影像編碼時，即使在維持相同視訊品質標準下，也能大幅節省傳輸頻寬和存儲空間。這樣的優勢使得觀眾無論是在線串流還是觀看存儲於本地的影片，都能享受到更加出色的視覺體驗。但由於採用了新的編碼技術如: 較符合影像特性的切割模式QTMTT(Quad-Tree and Multi Type Tree)、高精度、多方向以及多種類的預測模式等等，且利用(RateDistortion Optimization,RDO) 的方式找尋位元-失真率的最佳解，其編碼時間比起H.265/HEVC 高出了8 至10 倍，使得其較難應用在低運算能力之裝置上。因此，本篇論文提出了一個基於切割模式限制的CNN 神經網路達到加速H.266/VVC 的Inter-Frame Coding 編碼時間。我們設計的LAI-CNN 在切割模式的限制下進行預測，剔除掉冗餘的切割模式，有效節省Rate-Distortion Optimization 的窮舉時間。LAI-CNN 適用於三種Type，其中Type 1 著重在靜態/低動量區塊，應用在ClassE 下表現良好; Type 2 注重切割MTT 而來的subCU，涵蓋最多編碼區塊，整體加速最多; 而Type 3 則適用於切割QT 而來的subCU，加速表現落於三個Type 的中間，且失真表現最好。且LAI-CNN 模型在切割模式的限制下減少了判斷的選擇性以及複雜度，可以有效提升模型的效率，使得模型可以更小、更精簡。實驗結果顯示本研究所提出的加速方法與H.266/VVC 官方參考軟體(VTM-22.0) 在Random-Access 模式下相比，加速了36.35%∼49.20%，而BDBR 僅上升了1.36%∼3.47%。

H.266/VVC is seen as a milestone ushering in a new era. Its primary goal is to provide users with comprehensive high-quality video encoding services. Compared to its predecessor, H.265/HEVC, H.266/VVC exhibits superior performance at the same quality standard. Specifically, it offers higher encoding compression rates, enhancing efficiency by around
30% to 50%. This means that when using H.266/VVC for video encoding, significant bandwidth, and storage space can be saved even while maintaining the same video quality standard. Such advantages enable viewers to enjoy a superior visual experience whether streaming online
or watching locally stored videos. While VVC adopts new encoding techniques, such as: QTMTT split mode, higher resolution, more angle, and more prediction mode. Furthermore,
it utilizes Rate-Distortion Optimization (RDO) to find the optimal trade-off between bitrate and distortion. Its encoding time is 8 to 10 times higher than H.265/HEVC, making it more challenging to apply on devices with lower computational capabilities. Our designed LAI-CNN makes predictions under the constraint of split modes, eliminating redundant split modes effectively, thereby saving exhaustive time in Rate-DistortionOptimization. LAI-CNN applies to three types: Type 1 focuses on static/low-motion
blocks, performing well in ClassE sequences; Type 2 emphasizes subCUs derived from splitting MTT, covering the maximum number of encoded blocks, thus achieving the highest overall acceleration; Type 3 is suitable for subCUs derived from splitting QT, offering intermediate
acceleration performance among the three types and the best distortion performance. Furthermore, under the constraint of split modes, LAI-CNN reduces the selectivity and complexity of decision-making, effectively enhancing the efficiency of model, making it smaller and more streamlined.
Experiment results shows that our proposed method achieved 36.35%∼49.20% in time saving while BDBR only increased 1.36%∼3.47% under VTM-22.0 Random Access Configurations.

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