In this paper a novel semifragile watermarking scheme for images with multiple bands is presented. We propose to use the remote sensing image as a whole, using a vector quantization approach, instead of processing each band separately. This scheme uses the signature of the multispectral or hyperspectral image to embed the mark in it and detects a modification of the original image, e.g. a replacement of a part of the image into the same image or any other similar manipulation. A modification of the image means to modify the signature of each point, all the bands simultaneously, because in multispectral images it does not have sense to modify a single band of all those that compose the multispectral image. The original multispectral or hyperspectral image is segmented in three-dimensional blocks and, for each block, a tree structured vector quantizer is built, using all bands at the same time. These trees are manipulated using an iterative algorithm until the resulting image compressed by the manipulated tree satisfies all the imposed conditions by such tree, which represents the embedded mark. Each tree is partially modified accordingly to a secret key in order to avoid copy-and-replace attacks, and this key determines the internal structure of the tree and, also, the resulting distortion, in order to make the resulting image robust against near-lossless compression. The results show that the method works correctly with multispectral and hyperspectral images and detects copy-and-replace attacks from segments of the same image and basic modifications of the marked image.
This paper presents a benchmark assessment of the WAUC digital audio watermarking scheme, which relies on MPEG 1 Layer 3 compression to determine where and how the embedded mark must be introduced. The mark is embedded by modifying the magnitude of the spectrum at several frequencies which are chosen according to the difference between the original and the compressed audio content. The main advantage of the scheme is that the perceptual masking of the compressor is implicitly used and, thus, the scheme can be directly tested with different maskings by replacing the compressor. Since repeat coding of the mark is used, a majority voting scheme is applied to improve robustness. The scheme also uses a dual Hamming error correcting code for the embedded mark, which makes it possible to apply it for fingerprinting, achieving robustness against the collusion of two buyers. Several tuning parameters affect the embedding and reconstruction processes, the values of which are chosen according to the tuning guidelines obtained in previous works. In order to illustrate the robustness of the method, the WAUC scheme has been tested against several evaluation profiles, such as the attacks introduced in the Watermark Evaluation Testbed (WET) for audio.
In this paper we describe a proposal for multimedia and e-learning content description based on standards interoperability within a digital library environment integrated in a virtual campus. In any virtual e-learning environment, a complex scenario which usually includes a digital library or, at least, a repository of learning resources, different levels of description are needed for all the elements: learning resources, multimedia content, activities, roles, etc. These elements can be described using library, e-learning and multimedia standards, depending on the specific needs of each particular scenario of use, but this might lead to an undesirable duplication of metadata, and to inefficient content queries and maintenance. Furthermore, there is a lack of semantic descriptions which makes all these contents merely become digital objects in the digital library, without exploiting all the possibilities in a e-learning virtual environment. Due to its flexibility and completeness, we propose to use the MPEG-7 standard for describing all the learning resources in the digital library, combined with the use of an ontology for a formal description of the learning process. The equivalences of Dublin Core, LOM and MPEG-7 standards are outlined, and the requirements of a proposal for a MPEG-7 based representation for all the contents in the digital library and the virtual classroom are described. The intellectual property policies for content sharing both within and among organizations are also addressed. With such proposal, it would be possible to build complex multimedia courses from a repository of learning objects using the digital library as the core repository.
We perform an empirical evaluation of the lossy compression properties of the JPEG2000 standard for watermarking and fingerprinting purposes. The JPEG2000 standard is used as a basic tool for determining both how and where the embedded watermark should be placed in the image. The original image is slightly modified to generate a similar image (but indistinguishable by the observer), and the mark is embedded in the pixels presenting differences between both images. The reconstruction process uses the original and the modified images to detect the embedded mark in any possible attacked image, so the watermarking scheme is nonblind. Previous experiments show that the properties of the resulting watermarking scheme depend strongly on the transformation stage characteristics of the lossy image compression system. Several parameters related to the JPEG2000 standard are tested, in addition to the compression ratio determined by the desired bit rate: the wavelet transform, block size, and the number of levels of decomposition for each block. These parameters affect not only the capacity but also the robustness of the watermarking scheme, which depends on the number of differing pixels between the original image and the slightly modified one, and the distribution of such differences. For evaluation purposes, we use the Stirmark benchmark and the classical image corpus set for lossy image compression. We compare the impact on system performance of each of the JPEG2000 standard parameters for several kinds of attacks, namely, filtering (including sharpening); JPEG lossy compression; cropping; row and column removal; and a combination of rotation, cropping, and scaling. The false positive rate is also studied. Results show that the proposed watermarking scheme based on the JPEG2000 standard is robust against most of the classical image manipulation operation, and therefore, is suitable for watermarking purposes.
In this paper we evaluate the performance of several image
watermarking schemes applied to hyperspectral imaging. An image
watermarking scheme based on JPEG2000 which can be also used to
store and manipulate hyperspectral images is also described.
Different watermarking schemes are tested in order to determine
the suitability of each one for a specific hyperspectral image
environment. The impact of classical GIS operations (namely
zooming, cropping and compression) on the performance of each
watermarking scheme is measured in terms of capacity and
robustness. In order to do so, we study several possibilities for
watermarking hyperspectral images, as all hyperspectral image
bands should be taken into account. We also study the impact of
watermarking in image quality, measured as usual by PSNR, but also
by the degradation of classification performance. Compression,
classification and watermarking are closely related to each other
as decisions taken in one subject have a large impact on the
others. Our results show that the newcomer JPEG2000 standard is a useful tool for both hyperspectral imaging and copyright protection
purposes. The proposed watermarking scheme, which takes advantage
of JPEG2000 standard capabilities, can be considered to be robust
under the constraints defined by the integration of hyperspectral
imaging with geographical information systems. JPEG2000 extensions
defined by the standard related to this work are also considered.
High resolution images are nowadays a common source of data for many different applications; let us consider, for instance, hyperspectral images for remote sensing and geographic information systems. This kind of images allows for exhaustive analysis and provides good classification performance due to their high resolution (either bits per pixel, spatial, or spectral resolution). Nevertheless, this same high resolution, as well as their huge size, imposes a large demand of memory capability and channel bandwidth. To deal with this problem, lossy encoding of such images may be devised. Well known lossless and lossy image coding techniques have been used, but remote sensing and geographic information systems applications have some particular requirements that are not taken into account by the classical methods. There is therefore a need to investigate new approaches of image coding for these applications.
In this paper an empirical evaluation of the lossy compression
properties of the JPEG2000 standard for watermarking purposes is
performed. The JPEG2000 standard is used as a basic tool for
determining both how and where the embedded watermark should be
placed in the image. The original image is slightly modified in
order to generate a similar image (indistinguishable by the
observer), and the mark is embedded in the pixels presenting
differences between both images. Previous experiments show that
the properties of the resulting watermarking scheme depend
strongly on the transformation stage characteristics of the lossy
image compression system. Several parameters related to the
JPEG2000 standard are tested, in addition to the compression
ratio: the wavelet transform, block size, and the number of levels
of decomposition for each block. These parameters affect not only
the capacity but also the robustness of the watermarking scheme:
the number of different pixels between the original image and the
slightly modified one, and the distribution of such differences.
For evaluation purposes we use the Stirmark benchmark. We compare
the impact on system performance of each one of the JPEG2000
standard parameters, for several kinds of attacks, namely
filtering (including sharpening), JPEG lossy compression,
cropping, row and column removal, and a combination of rotation,
cropping and scaling.
Several well-known methods for lossy compression of still images are here analyzed to evaluate their performance for hyperspectral images.
The lossy compression methods discussed are the JPEG standard,
and four approaches based on the Wavelet Transform: the Embedded coding of ZeroTree wavelet coefficients, the Set Partitioning in Hierarchical Trees, a Lattice Vector Quantizer, and the new JPEG2K.
Experiments are first performed on corpuses of natural grayscale still images to provide a general framework of the performance of each method. Then experiments are performed on several hyperspectral images taken with CASI and AVIRIS sensors. Experiments show that it is possible to employ the basic lossy compression methods for hyperspectral image coding. The wavelet-based approaches produce results consistently better than the JPEG: JPEG can not achieve
compression ratios above 75:1; on the other side, with EZT, SPIHT and LVQ compression ratios of 250:1 or higher may be reached. For JPEG2K, higher compression ratios than JPEG may also be reached, but with a PSNR quality lower than the three other techniques. At compression ratios about 8:1, the wavelet methods yield results 1.5 dB better than those of JPEG. These results help to explain why JPEG2K standard uses the WT instead of the DCT.
In this paper we study the use of classifier combination for
improving the classification accuracy of AVIRIS data. Two types of
combination ensembles are used as high-level classifiers, cascading
and voting. Regarding the base-level classifiers, we use limited
depth decision trees and the nearest neighbor classifier (k-NN). The
final classification system uses a threshold parameter that allows
the user to specify a trade-off between classification accuracy and
the percentage of classified samples. Dimensionality reduction is
carried out by using decision trees in order to select the most
promising classification features, which will be used to build the
base-level classifiers. We also use classical statistical analysis
to measure correlation between spectral bands. A set of post-processing rules may be also applied to generate large homogeneous regions from the pixmap generated by the classifier: false spots and 'unknown' samples may be re-classified depending on their neighborhood. Experiments show that the combined use of cascading small decision trees and a voting scheme with a k-NN classifier, improves classification performance, when compared to a single classifier, while the the 'unknown' class allows us to identify the possible outliers present in the training set. The use of post-processing generates large regions which may be more useful for classification and interpretation.
In this paper several methods for image lossy compression are compared in order to find adaptive schemes that may improve compression performance for hyperspectral images under a classification accuracy constraint. Our goal is to achieve high compression ratios without degrading classification accuracy too much for a given classifier. Lossy compression methods such as JPEG, three-dimensional JPEG, a tree structured vector quantizer, a zero- tree wavelet encoder, and a lattice vector quantizer have been used to compress the image before the classification stage. Classification is carried out through classification trees. Two kinds of classification trees are compared: one- stage trees, which classify the input image using only a single classification stage; and multi-stage trees, which use a mixed class that delays the classification of problematic pixels for which the accuracy achieved in the current stage is not enough. Our experiments indicate that is is possible to achieve high compression ratios while maintaining the classification accuracy. It is also shown that compression methods that take advantage of the high band correlation of hyperspectral images provide better results and become more flexible for a real case scenario. As compared to one-stage trees, the employment of multi-stage trees increases the classification accuracy and reduces the classification cost.
In this paper we present a novel method for JPEG standard progressive operation mode definition scripts construction and evaluation. Our method allows the user to construct and evaluate several definition scripts without having to encode and decode a given image to test their validity, with a reduced cost.
In this paper, we present a progressive classification scheme for a document layout recognition system using three stages. The first stages, preprocessing, extracts statistical information that may be used for background detection and removal. The second stage, a tree based classified, uses a variable block size and a set of probabilistic rules to classify segmented blocks that are independently classified. The third, state, postprocessing, uses the label map generated in the second state with a set of context rules to label unclassified blocks, trying also to solve some of the misclassification errors that may have been generated during the previous stage. The progressive scheme used in the second and third stages allows the user to stop the classification process at any block size, depending on this requirements. Experiments show that a progressive scheme combined with a set of postprocessing rules increases the percentage of correctly classified blocks and reduces the number of block computations.
In this paper we propose a novel method for computing JPEG quantization matrices based on desired mean square error, avoiding the classical trial and error procedure. First, we use a relationship between a Laplacian source and its quantization error when uniform quantization is used in order to find a model for uniform quantization error. Then we apply this model to the coefficients obtained in the JPEG standard once the image to be compressed has been transformed by the discrete cosine transform. This allows us to compress an image using JPEG standard under a global MSE constraints and a set of local constraints determined by JPEG standard and visual criteria. Simulations show that our method generates better quantization matrices than the classical method scaling the JPEG default quantization matrix, with a cost lower than the coding, decoding and error measuring procedure.