最近，Sachnev等人提出了一種使用排序與預測法之高效能的可逆資料隱藏演算法，和現有的可逆資料隱藏演算法比較下，他們所提出的演算法可以嵌入更多數據並有較小的失真。在一般情況下，可嵌入的資料量和藏入資料後的圖像品質取決於可逆資料隱藏演算法預測的準確性。在本論文中，我們提出了基於梯度的邊緣方向預測方法以的改進版可逆資料隱藏演算法。由於所提出的基於梯度的邊緣方向預測方法能得到較準確的預測結果，所以預測誤差可形成更集中的拉普拉斯分佈。因此，我們的演算法可以得到較大的嵌入容量和產生更優質的藏入資料後圖像。對可逆資料隱藏演算法而言，決定適當的門檻值是一個十分關鍵的問題，因此我們設計了一個可以讓系統自動決定門檻值的方法來解決這個問題。此外，我們還提出了一個改良版的嵌入資料順序的決定策略以降低藏入資料後圖像的失真。透過在六張典型測試圖像驗證下，實驗結果顯示了所提出的演算法在可藏入之資料量與顯著的藏入資料後圖像之品質上的優越性能。

Recently, Sachnev et al. presented an efficient reversible data hiding (RDH) algorithm by using the sorting and prediction schemes. Their algorithm can embed more data with less distortion when compared with existing RDH algorithms. In general, the embedding capacity and marked image quality depend on the prediction accuracy in the RDH algorithm. In this paper, we present an improved RDH algorithm based on a new gradient-based edge direction prediction (GEDP) scheme. Since the proposed GEDP scheme can generate more accurate prediction results, the prediction errors tend to form a sharper Laplacian distribution. Therefore, the proposed algorithm can guarantee larger embedding capacity and produce better quality of marked images. The determination of appropriate thresholds is also a critical issue for a RDH algorithm, so we design a new systematic way to tackle this problem. In addition, a modified embedding order determination strategy is presented to reduce the distortion of a marked image. Based on six typical test images, experimental results demonstrate the superior properties of the proposed algorithm in terms of embedding capacity and marked image quality.

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