A scale space-variant filter (SVF) is proposed on the basis of Harris arithmetic operators, which can smoothly isolate noise efficiently at the situation of keeping edge information of the image. Comparing SVF with Gaussian filter under step jump signal and initial image input, the result indicates that SVF is better than Gaussian filter. Using SVF to detect feature points of an image, the experiment shows that feature points detected from SVF output contain more edge information. Using 2D space limitations, Euclidian distance limitation and angle limitation, we can eliminate redundant feature points so that all the useful feature points are distributed in all regions of the image evenly. From the result of the examination for noise-contained image, we can draw the conclusions that the new robust feature point detector can get more accurate position of feature points and the distribution of the points is more rational than that of the points without those limitations.
We propose a new frequency domain wavelet based watermarking technique. The key idea of our scheme is twofold: multi-tier solution representation of image and odd-even quantization embedding/extracting watermark. Because many complementary watermarks need to be hidden, the watermark image designed is image-adaptive. The meaningful and complementary watermark images was embedded into the original image (host image) by odd-even quantization modifying coefficients, which was selected from the detail wavelet coefficients of the original image, if their magnitudes are larger than their corresponding Just Noticeable Difference thresholds. The tests show good robustness against best-known attacks such as noise addition, image compression, median filtering, clipping as well as geometric transforms. Further research may improve the performance by refining JND thresholds.
Multiwavelets have orthogonality, compacted support and symmetry simultaneously, these properties are very important for signal processing. However, most of Multiwavelets require related prefilters. An approach to construction of symmetry/antisymmetry orthogonal filter is proposed and its corresponding balanced filter is constructed, no any prefilter is necessary. Experimental results prove its performance is superior to DGHM and CL multiwavelets, higher than Bi9/7.
KEYWORDS: Digital watermarking, Wavelet transforms, Data communications, Digital imaging, Image segmentation, Signal detection, Linear filtering, Data hiding, Wavelets, Image compression
Digital audio watermarking embeds inaudible information into digital audio data for the purposes of copyright protection, ownership verification, covert communication, and/or auxiliary data carrying. In this paper, we present a novel watermarking scheme to embed a meaningful gray image into digital audio by quantizing the wavelet coefficients (using integer lifting wavelet transform) of audio samples. Our audio-dependent watermarking procedure directly exploits temporal and frequency perceptual masking of the human auditory system (HAS) to guarantee that the embedded watermark image is inaudible and robust. The watermark is constructed by utilizing still image compression technique, breaking each audio clip into smaller segments, selecting the perceptually significant audio segments to wavelet transform, and quantizing the perceptually significant wavelet coefficients. The proposed watermarking algorithm can extract the watermark image without the help from the original digital audio signals. We also demonstrate the robustness of that watermarking procedure to audio degradations and distortions, e.g., those that result from noise adding, MPEG compression, low pass filtering, resampling, and requantization.
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