應用於車內視訊之光線適應性視訊壓縮編碼器設計

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姓名

賴學翰(Hsueh-Han Lai) 查詢紙本館藏

畢業系所

通訊工程學系

論文名稱

應用於車內視訊之光線適應性視訊壓縮編碼器設計
(An Illumination Adaptive Video Coding Scheme for In-vehicle Video Applications)

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摘要(中)

本論文設計一個提高乘客方便性之視訊通訊，可應用於車內駕駛人或乘客與辦公室內或是搭乘交通工具中的人之視訊通訊。但因為在無線通訊傳輸頻寬有限，所以此系統設計考量人類視覺特性的視訊編碼，降低非人臉區域的資料量。另一方面，由於光線常因環境的變化而改變，進而造成車內視訊內容有顯著的改變。因此，在此論文中，我們提出應用於車內視訊之光線適應視訊壓縮編碼器，包含光線校正、人臉偵測以及和人類視覺注意力為基礎的視訊壓縮三個階段，而光線校正方案整合Single Scale Retinex 和間隔權重直方圖分割法。由實驗結果可知，我們的光線校正方案有效提高車內視訊的人臉偵測率。此外，我們的視訊壓縮系統不僅可以降低資料量，並同時保持良好之視訊品質。

摘要(英)

With the advance of intelligent vehicle systems, drivers or passengers can keep interaction with people in fixed offices or other vehicles through visual communications. However, the illumination variations due to the changes of environments or weather conditions may significantly change the appearance of in-vehicle videos. Accordingly, the compression efficiency is much reduced even though the bandwidth of such wireless communications has been quite limited. There is pretty few previous work designed for efficient in-vehicle video compressions. Thus, in this paper, we propose an illumination adaptive video coding scheme for in-vehicle video applications. Since human faces are usually the most visually attended regions in such applications, this scheme consists of illumination correction, face detection, and the visual attention based video codec. The proposed illumination correction strategy combines the advantages of the single-scale Retinex (SSR) and the Interval weighted histogram separation (IWHS). The experimental results show that our illumination correction strategy effectively improves the face detection performance of in-vehicle videos. Moreover, the subjective visual quality of the proposed scheme outperforms that of H.264 with rate control since our scheme allocates bits by incorporating the human visual characteristics.

關鍵字(中)

★ 車內視訊
★ 光線
★ 視訊壓縮

關鍵字(英)

★ in-vehicle
★ video coding
★ illumination

論文目次

中文摘要 i
Abstract ii
誌謝 iii
目錄 iv
圖目錄 vi
表目錄 ix
第一章緒論 1
1.1 前言 1
1.2 研究背景 2
1.3 研究方法 3
1.4 論文架構 4
第二章以膚色為基礎之人臉偵測 5
2.1 人臉偵測簡介 5
2.2 膚色偵測介紹 7
2.2.1 色彩空間介紹 7
2.2.2 膚色分類演算法介紹 9
2.3 以CBCR色彩空間為基礎之人臉偵測方法 10
2.3.1 CHAI與NGAN所提出之膚色偵測方案 11
2.3.2 以膚色為基礎之人臉定位 13
2.4 總結 16
第三章光線校正 17
3.1 SINGLE SCALE RETINEX 17
3.2 可調式GAMMA校正演算法 20
3.3 權重直方圖分割法 26
3.4 本論文提出之間隔權重直方圖分割法 29
3.5 本論文提出之光線校正決策方案 35
3.6 總結 36
第四章人類視覺注意力為基礎之視訊壓縮 37
4.1 H.264視訊壓縮簡介 37
4.2 人類視覺注意力模型介紹 39
4.3 人類視覺注意力為基礎的視訊壓縮概況 40
4.4 本論文提出之應用於車內視訊之光線適應性視訊壓縮編碼器設計 42
4.5 總結 46
第五章實驗結果與討論 47
5.1 測試影片 47
5.2 實驗參數與環境 49
5.3 光線校正之系統效能比較 51
5.4 視訊壓縮效能與視訊品質效能之比較 63
5.4.1 視訊壓縮效能比較 63
5.4.2 主觀視覺測試比較 66
5.5 總結 69
第六章結論與未來展望 70
6.1 結論 70
6.2 未來展望 70
參考文獻 71

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指導教授

唐之瑋(Chih-Wei Tang)

審核日期

2008-7-17

推文