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Title: | 具時空間旁資訊分散式影像編碼 Distributed Video Codec with Spatiotemporal Side Information |
Authors: | Yueh-Ying Lee 李岳穎 |
Advisor: | 簡韶逸(Shao-Yi Chien) |
Keyword: | 分散式影像編碼, Distributed Video Codec, |
Publication Year : | 2016 |
Degree: | 碩士 |
Abstract: | 在這篇論文中,提出了一個具有時空間旁資訊分散式影像編碼的架構。這個提出的架構解決了以前分散式影像編碼在輸入的影片變動較大時影片壓縮效率不好的問題,這種變動很大的影片是在物聯網中由穿戴式相機或者是車用相機所拍到的影片無法避免的情形。實驗結果顯示,當和經典的 DISCOVER 分散式影像編碼比較時,平均具有 12.93%的壓縮率改進,而在變動大的影片中這些壓縮率改進更加的明顯。 除此之外,和DISCOVER 分散式編碼相比,平均所需要的編碼時間只要其 92.3%。
這篇論文所提出的架構透過整合時間和空間上的預測系統來達到上述的壓縮率改進。這篇論文所提的架構的貢獻在於確立了在分散式影像編碼中使用空間預測系統時所需要使用的編碼工具以及相關的流程。在所提出的架構中採用了超解析度技術來產生空間的旁資訊預測,並且採用了支持向量機分類器來動態從時間或者空間預測結構中選擇。除此之外,在幀、塊、以及係數的層級上各採用了不同的編碼模式選擇以更進一步的提升壓縮表現。 In this thesis, a DVC framework with spatiotemporal side information is proposed. The proposed framework addresses the shortage of poor compression performance for high-motion video sequences which is inevitable in Internet of Things(IoT) applications with wearable cameras and cameras in vehicles in previous DVC frameworks. Experimental results show that the average BD rate reduction of the proposed framework is 12.93% compared with DISCOVER DVC, and the coding gain is significant especially for high-motion sequences. More- over, the average computing complexity is only 92.26% of DISCOVER DVC. The proposed framework achieves compression performance gain by integrating both temporal and spatial prediction schemes into one framework. The major contribution lies in establishing coding flow and relevant coding tools for spatial prediction in DVC. Super-resolution technique is employed for spatially-predicted side information generation, and a support vector machine classifier is trained to adaptively select the coding structure between spatial and temporal prediction. In addition, coding mode selection at different granularities, including frame, block and coefficient levels, can further improve the coding performance. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/7969 |
DOI: | 10.6342/NTU201600275 |
Fulltext Rights: | 同意授權(全球公開) |
Appears in Collections: | 電子工程學研究所 |
Files in This Item:
File | Size | Format | |
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ntu-105-1.pdf | 2.94 MB | Adobe PDF | View/Open |
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