Lossless Image Compression via Predictive Coding of Discrete Radon Projections

International audienceThis paper investigates predictive coding methods to compress images represented in the Radon domain as a set of projections. Both the correlation within and between discrete Radon projections at similar angles can be exploited to achieve lossless compression. The discrete Radon projections investigated here are those used to define the Mojette transform first presented by Guedon et al. [Psychovisual image coding via an exact discrete Radon transform, in: T.W. Lance (Ed.), Proceedings of the Visual Communications AND Image Processing (VCIP), May 1995, Taipei, Taiwan, pp. 562-572]. This work is further to the preliminary investigation presented by Autrusseau et al. [Lossless compression based on a discrete and exact radon transform: a preliminary study, in: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol. II, May 2006, Toulouse, France, pp. 425-428]. The 1D Mojette projections are re-arranged as two dimensional images, thus allowing the use of 2D image compression techniques onto the projections. Besides the compression capabilities, the Mojette transforms brings an interesting property: a tunable redundancy. As the Mojette transform is able to both compress and add redundancy, the proposed method can be viewed as a joint lossless source-channel coding technique for images. We present here the evolution of the compression ratio depending on the chosen redundancy

A robust image watermarking technique based on quantization noise visibility thresholds

International audienceA tremendous amount of digital multimedia data is broadcasted daily over the internet. Since digital data can be very quickly and easily duplicated, intellectual property right protection techniques have become important and first appeared about fifty years ago (see [I.J. Cox, M.L. Miller, The First 50 Years of Electronic Watermarking, EURASIP J. Appl. Signal Process. 2 (2002) 126-132. [52]] for an extended review). Digital watermarking was born. Since its inception, many watermarking techniques have appeared, in all possible transformed spaces. However, an important lack in watermarking literature concerns the human visual system models. Several human visual system (HVS) model based watermarking techniques were designed in the late 1990's. Due to the weak robustness results, especially concerning geometrical distortions, the interest in such studies has reduced. In this paper, we intend to take advantage of recent advances in HVS models and watermarking techniques to revisit this issue. We will demonstrate that it is possible to resist too many attacks, including geometrical distortions, in HVS based watermarking algorithms. The perceptual model used here takes into account advanced features of the HVS identified from psychophysics experiments conducted in our laboratory. This model has been successfully applied in quality assessment and image coding schemes M. Carnec, P. Le Callet, D. Barba, An image quality assessment method based on perception of structural information, IEEE Internat. Conf. Image Process. 3 (2003) 185-188, N. Bekkat, A. Saadane, D. Barba, Masking effects in the quality assessment of coded images, in: SPIE Human Vision and Electronic Imaging V, 3959 (2000) 211-219. In this paper the human visual system model is used to create a perceptual mask in order to optimize the watermark strength. The optimal watermark obtained satisfies both invisibility and robustness requirements. Contrary to most watermarking schemes using advanced perceptual masks, in order to best thwart the de-synchronization problem induced by geometrical distortions, we propose here a Fourier domain embedding and detection technique optimizing the amplitude of the watermark. Finally, the robustness of the scheme obtained is assessed against all attacks provided by the Stirmark benchmark. This work proposes a new digital rights management technique using an advanced human visual system model that is able to resist various kind of attacks including many geometrical distortions

Redundant Image Representation via Multi-Scale Digital Radon Projection

International audienceA novel ordering of digital Radon projections co-efficients is presented here that enables progressive image reconstruc- tion from low resolution to full resolution. The digital Radon transform applied here is the Mojette transform first defined by Guedon et al. in [1]. The Mojette transform is a natural way to generate redundancy to any specified degree and has been demonstrated to be useful for redundant representation for robust data storage and transmission. Combining this with the wavelet transform facilitates compression, i.e., joint source-channel coding, along with the additional property of scalability

Towards a Simplified Perceptual Quality Metric for Watermarking Applications

International audienceThis work is motivated by the limitations of statistical quality metrics to assess the quality of images distorted in distinct frequency range. Common quality metrics, which basically have been designed and tested for various kind of global distortions, such as image coding may not be efficient for watermarking applications, where the distortions might be restricted in a very narrow portion of the frequency spectrum. We hereby want to propose an objective quality metric which performances do not depend on the distortion frequency range, but we nevertheless want to provide a simplified objective quality metric in opposition to the complex HVS based quality metrics recently made available. The proposed algorithm is generic (not designed for a particular distortion), and exploits the contrast sensitivity function (CSF) along with an adapted Minkowski error pooling. The results show a high correlation between the proposed objective metric and the mean opinion score (MOS). A comparison with relevant existing objective quality metrics is provided

Using deep learning for an automatic detection and classification of the vascular bifurcations along the Circle of Willis

Most of the intracranial aneurysms (ICA) occur on a specific portion of the cerebral vascular tree named the Circle of Willis (CoW). More particularly, they mainly arise onto fifteen of the major arterial bifurcations constituting this circular structure. Hence, for an efficient and timely diagnosis it is critical to develop some methods being able to accurately recognize each Bifurcation of Interest (BoI). Indeed, an automatic extraction of the bifurcations presenting the higher risk of developing an ICA would offer the neuroradiologists a quick glance at the most alarming areas. Due to the recent efforts on Artificial Intelligence, Deep Learning turned out to be the best performing technology for many pattern recognition tasks. Moreover, various methods have been particularly designed for medical image analysis purposes. This study intends to assist the neuroradiologists to promptly locate any bifurcation presenting a high risk of ICA occurrence. It can be seen as a Computer Aided Diagnosis scheme, where the Artificial Intelligence facilitates the access to the regions of interest within the MRI. In this work, we propose a method for a fully automatic detection and recognition of the bifurcations of interest forming the Circle of Willis. Several neural networks architectures have been tested, and we thoroughly evaluate the bifurcation recognition rate

1D-mosaics grouping using lattice vector quantization for a video browsing application

International audience1D-mosaics have been introduced as a tool for structuring and navigation in video content. These objects can be con- sidered as the spatio-temporal signatures of the video shots. Our work aims at grouping automatically the video shots into scenes using these signatures. The original method is based on the tree-structured lattice vector quantization of the 1D-mosaics. Because of the hierarchical structure of the code-books, they can be compared progressively, and lattice use is time efficient. Indexing retrieval results are given for two video sequences, and different mosaics are successively compared to each other in order to assess the presented scheme's effectiveness

Lossless Image Compression and Selective Encryption Using a Discrete Radon Transform

International audienceIn this paper we propose a new joint encryption and loss- less compression technique designed for large images 1 . The proposed technique takes advantage of the Mojette transform properties, and can easily be included in a distributed storage architecture. The basic crypto-compression scheme presented is based on a cascade of Radon projection which enables fast encryption of a large amount of digital data. Standard encryp- tion techniques, such as AES, DES, 3DES, or IDEA can be applied to encrypt very small percentages of high resolution images. As the proposed scheme uses standard encryption, and only transmits uncorrelated data along with the encrypted part, this technique takes benefit of the security related to the chosen encryption standard, here, we assess its performances in terms of processing time and compression ratio

Impact of the subjective dataset on the performance of image quality metrics

International audienceThe interest in objective quality assessment have significantly increased over the past decades. Several objective quality metrics have been proposed and made publicly available, moreover, several subjective quality assessment databases are distributed in order to evaluate and compare the metrics. However, several question arises: are the objective metrics behaviours constant across databases, contents and distortions? how significantly the subjective scores might fluctuate on different displays (i.e. CRT or LCD)? which objective quality metric might best evaluate a given distortion? In this article, we analyse the behaviour of four objective quality metrics (including PSNR) tested on three image databases. We demonstrate that the performances of the quality metrics can strongly fluctuate depending on the database used for testing. We also show the consistency of all metrics for two distinct displays

Comparing Autoencoder to Geometrical Features for Vascular Bifurcations Identification

The cerebrovascular tree is a complex anatomical structure that plays a crucial role in the brain irrigation. A precise identification of the bifurcations in the vascular network is essential for understanding various cerebral pathologies. Traditional methods often require manual intervention and are sensitive to variations in data quality. In recent years, deep learning techniques, and particularly autoencoders, have shown promising performances for feature extraction and pattern recognition in a variety of domains. In this paper, we propose two novel approaches for vascular bifurcation identification based respectiveley on Autoencoder and geometrical features. The performance and effectiveness of each method in terms of classification of vascular bifurcations using medical imaging data is presented. The evaluation was performed on a sample database composed of 91 TOF-MRA, using various evaluation measures, including accuracy, F1 score and confusion matrix.Comment: International Symposium on Image And Signal Processing and Analysis, Sep 2023, Rome, Ital

TSAR: Secure Transfer OF High Resolution Art Images

International audienceThe EROS (European Research Open System) database hosted at the Centre de Recherche et de Restauration des Musées de France (C2RMF) is one of the largest database in the world of Cultural Heritage that is widely recognized for its high resolution images. The French research project TSAR (Transfert Sécurisé d'images d'Art haute Resolution) aims to give the possibility to open this huge amount of art images in a secure and efficient way. For this purpose, we use a mixture of techniques to assure the security of the data involving cryptography and watermarking techniques as well as multi-resolution compression scheme together with a region-level representation. These algorithms are especially optimized for high resolution art images. In particular, this means that the quality of the transmitted images have to be not reduced, implying the use of lossless coding techniques. In this paper we present an overall scheme that provides an efficient, consistent solution for secure data browsing, viewing and transmitting, adoptable by any Cultural Heritage institution