近期，隱式神經表示（Implicit Neural Representation, INR）因成功解決進修式視訊壓縮中面對解碼速度慢的窘境，成為熱點研究標的目的，然而現有INR方式依然無法在壓縮效能上與最早進的學習式視訊緊縮方法匹敵，因此本文將以晉升緊縮機能為目標改良INR模子。
在模子架構方面，本文設計一個增強影像特徵模組，包括憑據視訊中特徵變化水平動態調劑GOP(Group of Pictures)區間，並插手時候嵌入輕量化，使得在同個GOP下的相同特徵能額外嵌入時候訊息，以及選取相對接近的樞紐特徵看成影像特徵，而非依靠固定的GOP肇端圖框作為特徵起原。同時引入自注意力模組CBAM（Convolutional Block Attention Module）加強關鍵特徵的關注。並在解碼器中透過帶有權重的殘差毗連（Skip Connect）， 以改善梯度活動，並實現輔助特徵與主要特徵之間的平衡。
在模型壓縮方面，本文憑據視訊的動態水平，對動靜態視訊採取分歧的剪枝策略，並結合模型剪枝策略，保存解碼過程的關頭層。在損失函數設計中，加入分階段頻域損失，統籌局部與全局特徵的表現。
在視訊表示法使命上，相較於本文基於Deng的模子，本文方式在模子巨細削減2%的情況下，PSNR指標還上升0.28。在視訊緊縮上，本文方式在PSNR指標超越基準方法、H265傳統視訊壓縮，和DCVC學習式視訊緊縮方法。
另外，不同於以往 INR 視訊緊縮方式均採取固定的練習方式，本文透過度析視訊的特征，進而動態調劑練習策略，並經由過程消融嘗試驗證其有用性。

In recent years, Implicit Neural Representation (INR) has become a popular research direction because it successfully solves the slow decoding speed in learned video compression.
However, the existing INR methods are still not able to match the state-of-the-art learned video compression method in terms of compression performance. Therefore, we aim to improve INR models with the goal of improving compression performance.
In terms of model architecture, we design an enhanced image feature module, which dynamically adjusts Group of Pictures (GOP) interval based on the feature variation in a video. Additionally, we introduce lightweight temporal embeddings to embed time information into features within the same GOP. Instead of relying on fixed GOP initial frames as feature sources, we select relatively close key features as image features. Meanwhile, we introduce self-attention module CBAM (Convolutional Block Attention Module) to strengthen attention to key features.
Moreover, in the decoder, we employ skip connections with weight to improve gradient flow and achieve a balance between auxiliary and primary features.
In terms of model compression, we propose a dynamic pruning strategy based on the dynamic degree of video, applying different pruning strategies for static and dynamic videos. We also combine model pruning strategy to retain key layers during the decoding process. In loss function design, we introduce a stage-wise frequency domain loss to optimize both local and global feature representations.
For video representation tasks, compared to Deng’s model, our proposed method reduces model size by 2%, while improving the PSNR metric by 0.28dB. For video compression, our method outperforms the baseline method, traditional video compression H.265, and learned video compression DCVC in PSNR.
Notably, unlike previous INR-based video compression methods that use fixed training methods, this study through analyzes the characteristics of video to adjust the training strategy. Furthermore, we validate the effectiveness of our approach through ablation experiments.

摘要 i
2.1 顯式視訊示意與隱式神經透露表現 5
2.2 隱式神經暗示的嵌入類型 7
2.3 隱式神經示意的解碼器架構 9
2.4 隱式神經透露表現的模子緊縮流程 11
2.5 隱式神經默示的訓練策略 11
2.6 總結文獻方式 12
3 第三章 本文提出方法 14
3.1 本文模子架構 14
3.1.1 增強影象特徵模組 15
3.1.2 多解析度時候網格 18
3.1.3 特徵融會模組 19
3.1.4 自注意力模組 (CBAM) 20
3.1.5 光流指導圖框聚合 21
3.1.6 解碼器 23
3.2 視訊緊縮流程 24
3.2.1 量化感知練習 25
3.2.2 特徵量化 25
3.2.3 剪枝 26
3.2.4 權重編碼 27
3.3 損失函數 27
3.3.1 加強損失函數 27
3.3.2 分階段損失函數 28
4 第四章 實驗後果 30
4.1 實行設置 30
4.1.1 實驗情況 30
4.1.2 資料集 31
4.1.3 超參數 32
4.1.4 評估方式 35
4.2 視訊透露表現法實行後果 35
4.3 視訊緊縮實行成績 36
4.4 消融嘗試 40
4.4.1 模型架構 40
4.4.2 損失函數 41
4.4.3 模子緊縮 42
5 第五章 結論 45
參考文獻 47

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