隨著製程的進步，物聯網科技發展更加迅速，其中監控系統扮演著重要的角色，除了提升生活環境的安全性外也提供生活的便利性，但隨著使用率提高，這些視訊處理電路的穩定度也顯得更加重要。由於監控系統內的視訊電路可能會隨著時間老化或因為外力產生錯誤，使得產生出來的視訊不清楚或無法辨識，進而產生安全上的隱憂。另一方面，由於監控系統的視訊來源並不只一個，若每出問題就派人維修，所需要消耗的維修成本可能也不低。所幸視訊處理電路發生錯誤時並不一定會造成監控系統失效，若錯誤較為輕微，在視訊上可能不會有太大影響，仍舊能夠辨別物件。所以如何有效率地評估視訊來源之可靠度是一個極為重要的議題，因為其有助於降低維護成本，若是能更進一步提升來源的可靠度，則更有助於延長視訊處理電路的使用壽命。
在本論文中，我們針對視訊解碼電路進行詳細的錯誤模擬，並且分析錯誤對於視訊的影響，以及說明現有視訊修復方法在有錯解碼電路上會遭遇的問題。我們也根據分析結果找到一個可以有效提升錯誤電路視訊品質的方法，以及此方法有效的區間，並且在此區間能提升77%以上的錯誤容誤能力。
除此之外，根據分析結果我們提出一種提升電路容誤能力的方法。值得一提的是，此方法整合了本實驗室所開發的無參考容誤測試方法，使之比起使用文獻中已有之視訊品質評估方法，我們所提方法能夠降低90%測試時間以及節省99%所需記憶體空間。本論文所提出的方法除了可以進行電路的容誤測試，更可進一步提升電路容誤能力。

With the advances in semiconductor technology, Internet of Things (IoT) devices and applications have been developing rapidly. Among these devices and applications, surveillance systems play an important role in environmental security and also lead to more convenient life. So far surveillance systems have become more and more popular, which makes enhancing the reliability of the associated video processing circuits more critical. Due to aging effects or external disturbances, videos may become erroneous and thus even unrecognizable. On the other hand, since surveillance systems usually have a large number of video sources, manual checking and repairing each video source by the maintenance staff may incur huge cost. Fortunately, errors in video processing circuits may not necessarily invalidate the entire surveillance system. Minor errors may not affect the success of identifying objects of interests in the video. As a result, how to efficiently assess the reliability of video sources is a critical issue to be addressed. Moreover, if the reliability can be further enhanced, the lifetime of video processing circuits can be effectively extended.
In this thesis, we carry out detailed error simulations for a video decoder, and analyze the impact of errors on the quality of videos. We also investigate the problems that recent video repair methods would encounter in repairing erroneous videos resulting from faulty decoders. Accordingly we develop an effective video quality enhancement technique and identify the scenarios that the developed technique is applicable. In these scenarios at least 77% of unacceptable erroneous videos can become acceptable.
In addition, we also propose an error-tolerability enhancement flow for videos. In particular, this flow integrates with the no-reference error-tolerability test method developed by our research group. This integration leads to 90% test time and 99% needed memory space reduction when compared with the previous video quality assessment method. In addition to testing the error-tolerability of the circuit, our proposed flow can also further improve the error-tolerability of the circuit.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
目錄 v
圖目錄 vii
表目錄 ix
第一章 概論 1
1.1 研究動機 1
1.2 研究貢獻 1
1.3 論文大綱 2
第二章 研究背景及相關文獻回顧 3
2.1 H.264/AVC視訊壓縮編碼標準簡介 3
2.2 視訊品質評估參數 5
2.2.1 峰值訊噪比Peak Signal Noise Ratio (PSNR) 5
2.2.2 結構相似性Structural Similarity (SSIM) 6
2.3 容誤 Error-Tolerance 7
2.4 既有視訊修復方法 8
第三章 有錯電路與多I-frame之視訊分析 10
3.1 簡介 10
3.2 錯誤視訊產生流程 11
3.2.1 錯誤注入 11
3.2.2 JM Encoder參數設定 13
3.2.3 測試視訊 14
3.3 單一I-frame錯誤視訊分析 16
3.4 多I-frame視訊 19
3.4.1 I-frame insertion 19
3.4.2 I-frame數量與檔案大小分析 21
3.5 多I-frame錯誤視訊分析 22
3.5.1 錯誤I-frame與有效區間 22
3.5.2 有效區間與分級 30
第四章 針對視訊之容誤提升與評估方法 37
4.1 視訊容誤提升方法 37
4.2 高效率之無參考視訊容誤測試方法 39
4.2.1 邊緣偵測Edge Detection 39
4.2.2 邊緣數量變化 41
4.3 實現視訊容誤提升方法 44
4.4 實驗結果與比較 50
第五章 硬體電路實現與結果討論 52
5.1 視訊品質提升方法之硬體實現目標 52
5.1.1 頻率 52
5.1.2 面積 52
5.1.3 功率消耗 52
5.2 硬體架構 52
5.3 硬體實現結果 54
5.4 結果與討論 54
第六章 結論與未來展望 56
第七章 參考文獻 57

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