Watermarking techniques are widely used to protect the secret document. In some valuable literatures, most of them concentrate on the binary data watermarking by using comparisons of an original image and a watermarked image to extract the watermark. In this paper, an efficient watermarking algorithm is presented with two-layer hidden for gray-level image watermarking. In the first layer, the key information is found based on the codebook concept. Then the secret key is further hidden to the watermarked image adopting the encryption consisting of spatial distribution in the second layer. The simulations demonstrate that the watermarking information is perceptually invisible in the watermarked image. Moreover, the gray-level watermark can be extracted by referring key parameters rather than the original image, and the extracting quality is very good.

In this paper, we propose a fast algorithm to realize parallel median filter for processing 1-D and 2-D signal. In the proposed pipelined architecture, m-passes are employed for filtering signal while word resolution is m bits. One pass employs one processing element (PE), and the number of PEs is independent of the number of samples. Therefore, we only need m PEs for real-time operation. With 8-bits resolution, the system gate-count is less than 5 k. Moreover, this median architecture could be easily modified to consist of the programmable feature that may choose the better sampling number to filter signal. It should be also noted that our proposed processing flow has a progressive property, which is very suitable for bandwidth-limited channel application.

Wavelet-based features with simplicity and high efficacy have been used in many pattern recognition (PR) applications. These features are usually generated from the wavelet coefficients of coarse levels (i.e., high octaves) in the discrete periodized wavelet transform (DPWT). In this paper, a new 1-D non-recursive DPWT (NRDPWT) is presented for real-time high octave decomposition. The new 1-D NRDPWT referred to as the 1-D RRO-NRDPWT can overcome the word-length-growth (WLG) effect based on two strategies, resisting error propagation and applying a reversible round-off linear transformation (RROLT) theorem. Finite precision performance analysis is also taken to study the word length suppression efficiency and the feature efficacy in breast lesion classification on ultrasonic images. For the realization of high octave decomposition, a segment accumulation algorithm (SAA) is also presented. The SAA is a new folding technique that can reduce multipliers and adders dramatically without the cost of increasing latency.