In the digital information age, digital content (audio, image, and video) can be easily copied, manipulated, and distributed. Copyright protection and content authentication of digital content has become an urgent problem to content owners and distributors. Digital watermarking has provided a valuable solution to this problem. Based on its application scenario, most digital watermarking methods can be divided into two categories: robust watermarking and fragile watermarking. As a special subset of fragile watermark, reversible watermark (which is also called lossless watermark, invertible watermark, erasable watermark) enables the recovery of the original, unwatermarked content after the watermarked content has been detected to be authentic. Such reversibility to get back unwatermarked content is highly desired in sensitive imagery, such as military data and medical data. In this paper we present a reversible watermarking method based on an integer wavelet transform. We look into the binary representation of each wavelet coefficient and embed an extra bit to expandable wavelet coefficient. The location map of all expanded coefficients will be coded by JBIG2 compression and these coefficient values will be losslessly compressed by arithmetic coding. Besides these two compressed bit streams, an SHA-256 hash of the original image will also be embedded for authentication purpose.
KEYWORDS: Image compression, Digital watermarking, Digital imaging, Image quality, Modulation, Visualization, Visual process modeling, Visual compression, Image restoration, Binary data
In the digital information age, digital content (audio, image, and video) can be easily copied, manipulated, and distributed. Copyright protection and content authentication of digital content has become an urgent problem to content owners and distributors. Digital watermarking has provided a valid solution to this problem. Based on its application scenario, most digital watermarking methods can be divided into two categories: robust watermarking and fragile watermarking. Here, we will concentrate on fragile watermarking of digital images, which is for image content authentication. Our fragile watermarking method is heavily based on the new image compression standard JPEG 2000. We choose a compressed bit stream from JPEG 2000 as the hash of an image, and embed the hash back to the image. The exceptional compression performance of JPEG 2000 solves the tradeoff between small hash size and high hash confidence level. In the authentication stage, the embedded compressed bit stream will be extracted. Then it will be compared with the compressed bit stream of the image to be authenticated. The authentication decision comes from the comparison result. Besides content authentication, we will also show how to employ this watermarking method for hiding one image into another.
With the large amount of image data that can be produced in real-time by new synthetic aperture radar (SAR) platforms, such as Global Hawk, compression techniques will be needed for both transmission and storage of this data. Also to keep image analysts (IA's) from being overwhelmed, high-speed automatic target cueing and/or recognition (ATC, ATR) systems will be needed to help exploit this large amount of data in real-time. Past SAR image compression studies have used subjective visual ratings and/or statistical measures such as mean-squared-error (MSE) to compare compression performance. Statistical metrics are much more appealing than unreproducible biased visual interpretations. However, the use of statistical metrics, such as MSE, has practical limitations on SAR imagery due to the high frequency speckle noise that is characteristic. In this case, the MSE metric is dominated by how well the noise speckle is preserved -- a statistic that is of no consequence. Since the large amount of data that dictates the need for compression also dictates the need for ATR, a meaningful statistic would be ATR performance. This ATR performance metric would emphasize how well pixels on target are preserved. Therefore, we have investigated ATR performance using a wavelet compression technique, since this technique has achieved very high compression on other types of imagery. We have used the Rice University Computational Mathematics Laboratory's wavelet compression algorithm in conjunction with a 'synthetic discriminant function' (SDF) based ATR algorithm. The SDF technique was developed at Carnegie Mellon University and successfully applied to SAR imagery by the Northrop Grumman Science & Technology Center. This combination allows ATR performance to be parameterized as a function of compression rate. The SAR data used for this research was taken from the public-released MSTAR target and clutter data set. We show results for both target detection and target identification versus false alarms for varying compression rates.
The purpose of this paper is to study signal denoising by thresholding coefficients of undecimated discrete wavelet packet transforms (UDWPT). The undecimated filterbank implementation of UDWPT is first considered, and the best basis selection algorithm that prunes the complete undecimated discrete wavelet packet binary tree is studied for the purpose of signal denoising. Distinct from the usual approach which selects the best subtree based on the original (unthresholded) transform coefficients, our selection is based on the thresholded coefficients, since we believe discarding the small coefficients permits to choose the best basis from the set of coefficients that will really contribute to the reconstructed signal. Another feature of the algorithm is the thresholding scheme. To threshold coefficients which are correlated differently from scale to scale and from band to band, a uniform threshold is not appropriate. Alternatively, two scale-band-dependent thresholding schemes are designed: a correlation-dependent model and a Monte Carlo simulation-based model. The cost function for the pruning algorithm is specifically designed for the purpose of signal denoising. We consider it profitable to split a band if more noise can be discarded by thresholding while signal components are preserved. So, higher SNR is desirable in the process of selection. Experiments conducted for 1D and 2D signals shows that the algorithm achieves good SNR performance while preserving high frequency details of signals.
The wavelet transform gives a compact multiscale representation of a digital image and provides a hierarchical structure which is well suited for post- processing. With its good localization property in both the spatial domain and the frequency domain, wavelet-based image compression has gained a huge success in the past several years. In this paper we present a wavelet-based image codec specially designed for an automatic target recognition (ATR) system. Due to the large amount of data size, a `good' image compression algorithm is both necessary and important as a pre-processing stage for an ATR system, especially in a `real-time' processing situation. We incorporate a constant false alarm rate (CFAR) detector into an embedded image compression algorithm to efficiently code target pixels exclusively in the bit stream. The new image codec, which is enhanced by the CFAR feature, clearly exhibits (potential) targets in the decompressed image. Another new feature of this codec is a wavelet-interpretation of the CFAR detector, a multiscale representation of the CFAR values in the wavelet domain.
An orthogonal m-band discrete wavelet transform has an O(m2) complexity. In this paper, we present a fast implementation of such a discrete wavelet transform. In an orthonormal m-band wavelet system, the vanishing moments and orthogonality conditions are imposed on the scaling filter only. Given a scaling filter, one can design the other m-1 wavelet filters. It is well-known that there are infinitely many solutions in such designing procedure. Here we choose one specific type of solutions and implement the corresponding wavelet transform in a scheme which has complexity O(m). Thus for any scaling filter, one can always construct a full orthogonal m-band wavelet matrix with an O(m) discrete wavelet transform.
In this paper we will discuss the performance of a new wavelet based embedded compression algorithm on synthetic aperture radar (SAR) image data. This new algorithm uses index coding on the indices of the discrete wavelet transform of the image data and provides an embedded code to successively approximate it. Results on compressing still images, medical images as well as seismic traces indicate that the new algorithm performs quite competitively with other image compression algorithms. The evaluation for SAR image compression of it will be presented in this paper. One advantage of the new algorithm presented here is that the compressed data is encoded in such a way as to facilitate processing in the compressed wavelet domain, which is a significant aspect considering the rate at which SAR data is collected and the desire to process the data 'near real time'.
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