基於改良移動預測之最佳化影像失真保護方法

Abstract

不對等保護(Unequal Error Protection)是一種透過重要度來改變保護資料比例的方法。當網路在傳輸時封包的掉落會造成影像失真，使用上述方法即可降低錯誤機率的發生。本篇論文中，我們提出了一個透過『不對等保護』來最佳化影片的位元率-失真(Rate-Distortion)，並且使用了Flexible Macroblock Ordering(FMO)的方式，讓比較重要的影格(Macroblock)可以放在相同的群組(Slice Group)中，且給予其較高的保護。我們參考了Rose所提出的ROPE方法[16]來進行分類，透過影格的失真率，來決定他在影像中的重要度；也參考了Shih所提出的方法[15]將影格分類到不同的群組中。接著以最佳化影片的位元率-失真演算法，來決定分配每一個群組的前向糾錯碼(Forward Error Correction)的數量，讓較重要的資料給予更高的保護。我們還更改『全零區塊動態預測』方法用來進行收斂性動態預測，使動態預測能夠提高『不對等保護』的效能。上述的收斂性動態預測方法是Shih所提出[15]，是以修改過的動態預測來選取較重要的影格，來確保影格有最好的前向糾錯保護。然而，Shih的方法有可能會造成資料量增加，而影響到可用的FEC數量。因此，我們修改了其方法，實驗結果也顯示出我們提出的『全零區塊動態』預測方法有更優良的效能。

Unequal Error Protection (UEP) is a method to efficiently protect the more important portions of the data. It has been widely used for video transmitting over the error-prone channels. In this thesis, we present a Rate-Distortion (R-D) optimized UEP scheme for video transmission over error-prone wireless networks. Our UEP scheme is incorporated with Flexible Macroblock Ordering (FMO), by which the more important macroblocks in a picture are grouped into the same slice groups and protected more than others. We had adopted the ROPE method, proposed by Rose [16], for macroblock distortion estimation in order to determine the importance of each macroblock. Also we adopted a modified dispersed FMO mode proposed by Shih [15] for classifying macroblocks into slice groups. And then, an R-D optimized FEC allocation algorithm is proposed to efficiently protect the more important slice groups than others. In addition, a modified Converged Motion Estimation (CME) based on the concept of all-zero-block (AZB) detection algorithm is proposed to further improve the proposed UEP. The CME is a method proposed in [15]. It makes macroblocks been referenced in a skewed manner so that highly important macroblocks are converged on only few and the use of FEC is efficient. However, CME may result in an increase in the source bit rate. We modify the CME to cope with this problem and our experiments on many video sequences show promising results.