Efficient Reversible Watermarking Based on Adaptive Prediction-Error Expansion and Pixel Selection

TLDR

Prediction‑error expansion (PEE) is a reversible watermarking technique that embeds large payloads into digital images with low distortion. This paper proposes an efficient reversible watermarking scheme that extends PEE with adaptive embedding and pixel‑selection strategies. The scheme adaptively embeds 1–2 bits into expandable pixels according to local complexity and selects smooth‑area pixels for embedding while leaving rough pixels unchanged, producing a sharper prediction‑error histogram. These improvements reduce maximum pixel modification, increase payload capacity, enhance visual quality, and experimentally outperform conventional PEE and other state‑of‑the‑art methods.

Abstract

Prediction-error expansion (PEE) is an important technique of reversible watermarking which can embed large payloads into digital images with low distortion. In this paper, the PEE technique is further investigated and an efficient reversible watermarking scheme is proposed, by incorporating in PEE two new strategies, namely, adaptive embedding and pixel selection. Unlike conventional PEE which embeds data uniformly, we propose to adaptively embed 1 or 2 bits into expandable pixel according to the local complexity. This avoids expanding pixels with large prediction-errors, and thus, it reduces embedding impact by decreasing the maximum modification to pixel values. Meanwhile, adaptive PEE allows very large payload in a single embedding pass, and it improves the capacity limit of conventional PEE. We also propose to select pixels of smooth area for data embedding and leave rough pixels unchanged. In this way, compared with conventional PEE, a more sharply distributed prediction-error histogram is obtained and a better visual quality of watermarked image is observed. With these improvements, our method outperforms conventional PEE. Its superiority over other state-of-the-art methods is also demonstrated experimentally.