Exploiting root-mean-square time-frequency structure for multiple-image optical compression and encryption

International audienceWe report on a new algorithm to compress and encrypt simultaneously multiple images (target images). This method, which is based upon a specific spectral multiplexing (fusion without overlapping) of the multiple images, aims to achieve a single encrypted image, at the output plane of our system, that contains all information needed to reconstruct the target images. For that purpose, we divide the Fourier plane of the image to transmit into two types of area, i.e., specific and common areas to each target image. A segmentation criterion taking into account the rootmean- square duration of each target image spectrum is proposed. This approach, which consists of merging the input target images together (in the Fourier plane) allows us to reduce the information to be stored and/or transmitted (compression) and induce noise on the output image (encryption). To achieve a good encryption level, a first key image (containing biometric information and providing the intellectual property of the target images) is used. A second encryption key is inserted in the Fourier plane to ensure a relevant phase distribution of the different merged spectra. We also discuss how the encoding information can be optimized by minimizing the number of bits required to encode each pixel. © 2010 Optical Society of Americ

Optical image compression and encryption methods

International audienceOver the years extensive studies have been carried out to apply coherent optics methods in real-time communications and image transmission. This is especially true when a large amount of information needs to be processed, e.g., in high-resolution imaging. The recent progress in data-processing networks and communication systems has considerably increased the capacity of information exchange. However, the transmitted data can be intercepted by nonauthorized people. This explains why considerable effort is being devoted at the current time to data encryption and secure transmission. In addition, only a small part of the overall information is really useful for many applications. Consequently, applications can tolerate information compression that requires important processing when the transmission bit rate is taken into account. To enable efficient and secure information exchange, it is often necessary to reduce the amount of transmitted information. In this context, much work has been undertaken using the principle of coherent optics filtering for selecting relevant information and encrypting it. Compression and encryption operations are often carried out separately, although they are strongly related and can influence each other. Optical processing methodologies, based on filtering, are described that are applicable to transmission and/or data storage. Finally, the advantages and limitations of a set of optical compression and encryption methods are discussed

Joint Optimization of Low-power DCT Architecture and Effcient Quantization Technique for Embedded Image Compression

International audienceThe Discrete Cosine Transform (DCT)-based image com- pression is widely used in today's communication systems. Signi cant research devoted to this domain has demonstrated that the optical com- pression methods can o er a higher speed but su er from bad image quality and a growing complexity. To meet the challenges of higher im- age quality and high speed processing, in this chapter, we present a joint system for DCT-based image compression by combining a VLSI archi- tecture of the DCT algorithm and an e cient quantization technique. Our approach is, rstly, based on a new granularity method in order to take advantage of the adjacent pixel correlation of the input blocks and to improve the visual quality of the reconstructed image. Second, a new architecture based on the Canonical Signed Digit and a novel Common Subexpression Elimination technique is proposed to replace the constant multipliers. Finally, a recon gurable quantization method is presented to e ectively save the computational complexity. Experimental results obtained with a prototype based on FPGA implementation and com- parisons with existing works corroborate the validity of the proposed optimizations in terms of power reduction, speed increase, silicon area saving and PSNR improvement

A New Robust and Discriminating Method for Face Recognition Based on Correlation Technique and Independent Component Analysis Model

International audienceWe demonstrate a novel technique for face recognition combined the independent component analysis (ICA) model with the optical correlation technique. Our approach relies on the performances of a strongly discriminating optical correlation method along with the robustness of the ICA model. Simulations were performed to illustrate how this algorithm can identify a face with images from the Pointing Head Pose Image Database (PHPID). While maintaining algorithmic simplicity, this approach based on ICA representation significantly increases the true recognition rate compared to that obtained with an all numerical ICA identity recognition method, that we recently developed, and with another based on optical correlation and a standard composite filter

Performance indexes of BSS for real-world applications

Publication in the conference proceedings of EUSIPCO, Florence, Italy, 200

A VLSI implementation of a new simultaneous images compression and encryption method

International audienceIn this manuscript, we describe a fully pipelined single chip architecture for implementing a new simultaneous image compression and encryption method suitable for real-time applications. The proposed method exploits the DCT properties to achieve the compression and the encryption simultaneously. First, to realize the compression, 8-point DCT applied to several images are done. Second, contrary to traditional compression algorithms, only some special points of DCT outputs are multiplexed. For the encryption process, a random number is generated and added to some specific DCT coefficients. On the other hand, to enhance the material implementation of the proposed method, a special attention is given to the DCT algorithm. In fact, a new way to realize the compression based on DCT algorithm and to reduce, at the same time, the material requirements of the compression process is presented. Simulation results show a compression ratio higher than 65% and a PSNR about 28 dB. The proposed architecture can be implemented in FPGA to yield a throughput of 206 MS/s which allows the processing of more than 30 frames per second for 1024x1024 images

Implementation techniques of high-order FFT into low-cost FPGA

International audienceIn this paper, our objective is to detail know-how and techniques that can help the designer of electronic circuits to develop and to optimize their own IP in a reasonable time. For this reason, we propose to optimize existing FFT algorithms for low-cost FPGA implementations. For that, we have used short length structures to obtain higher length transforms. Indeed, we can obtain a VLSI structure by using log4 (N) 4-point FFTs to construct N-point FFT rather than (N/8) log8 (N) 8-point FFTs. Furthermore, two techniques are used to yield with VLSI architecture. Firstly, the radix-4 FFT is modified to process one sample per clock cycle. Secondly, the memory is shared and divided into 4 parts to reduce the consumed resources and to improve the overall latency. Comparisons with commercial IP cores show that the low area architecture presents the best compromise in terms of speed/are

Towards all-numerical implementation of correlation

International audienceInterestingly, the past 20 years have provided us many examples of optical correlation methods for pattern recognition, e.g. VanderLugt correlator (VLC). In recent years, hybrid techniques, i.e. numerical implementation of correlation, have been also considered an alternative to all-optical methods because they show a good compromise between performance and simplicity. Moreover, these correlation methods can be implemented using an all-numerical and reprogrammable target such as the graphics processor unit (GPU), or the field-programmable gate array (FPGA). However, this numerical procedure requires realizing two Fourier Transforms (FT), a spectral multiplication, and a correlation plane analysis. The purpose of this study is to compare the performances of a numerical correlator based on the fast Fourier transform (FFT) with that relying on a simulation of the Fraunhofer diffraction. Different tests using the Pointing Head Pose Image Database (PHPID) and considering faces with vertical and horizontal rotations were performed with the code MATLAB. Tests were conducted with a five reference optimized composite filter. The receiving operating characteristics (ROC) curves show that the optical FT simulating the Fraunhofer diffraction leads to better performances than the FFT. The implications of our results for correlation are discussed

Exploring underwater target detection by imaging polarimetry and correlation techniques

International audienceUnderwater target detection is investigated by combining active polarization imaging and optical correlation-based approaches. Experiments were conducted in a glass tank filled with tap water with diluted milk or seawater and containing targets of arbitrary polarimetric responses. We found that target estimation obtained by imaging with two orthogonal polarization states always improves detection performances when correlation is used as detection criterion. This experimentally study illustrates the potential of polarization imaging for underwater target detection and opens interesting perspectives for the development of underwater imaging systems

Reconnaissance des objets manufacturés dans des vidéos sous-marines

Les mines sous marines sont très utilisées dans les conflits. Pour contrer cette menace, les marines s'équipent de moyens de lutte anti-mine autonomes afin d'éviter l'intervention humaine. Une mission de guerre des mines se découpe en quatre étapes distinctes : la détection des objets, la classification et l'identification puis la neutralisation. Cette thèse propose des solutions algorithmiques pour l'étape d'identification par caméra vidéo. Le drone d'identification connaît la position approximative de l'objet à identifier. La première mission de ce drone est de re-détecter l'objet avant de le classifier et de l'identifier. Le milieu sous-marin perturbe les images acquises par la caméra (absorption, diffusion). Pour faciliter la détection et la reconnaissance (classification et identification), nous avons prétraité les images. Nous avons proposé deux méthodes de détection des objets. Tout d'abord nous modifions le spectre de l'image afin d'obtenir une image dans laquelle il est possible de détecter les contours des objets. Une seconde méthode a été développée à partir de la soustraction du fond, appris en début de séquence vidéo. Les résultats obtenus avec cette seconde méthode ont été comparés à une méthode existante. Lorsqu'il y a une détection, nous cherchons à reconnaître l'objet. Pour cela, nous utilisons la corrélation. Les images de référence ont été obtenues à partir d'images de synthèse 3D des mines. Pour les différentes méthodes utilisées, nous avons optimisés les résultats en utilisant les informations de navigation. En effet, selon les déplacements du drone, nous pouvons fixer des contraintes qui vont améliorer la détection et réduire le temps de calcul nécessaire à l'identification.To avoid the underwater mine threat and to limit human interventions, navies use autonomous underwater vehicles. An underwater mine warfare mission is divided into four steps : object detection, classification, identification and neutralization. This PhD thesis proposes algorithmic solutions for the identification step done with a video camera. Thanks to the detection step, the identification vehicle knows approximately the object position. First, the vehicle has to detect and position this object exactly. Then it will be classified and identified. The underwater medium affects the images acquired with a video camera through absorption and scattering. The first step of our algorithm is to preprocess the images to help the detection and recognition (classification and identification) steps.We have proposed two detection methods. The first one consists in modifying image spectrum in order to obtain an image in which we will be able to detect edges of objects. The second method, based on region segmentation, has been developed from background subtraction methods. The background image has been learned at the beginning of the video when there is no object. The results of the latter have been compared to those obtained with a state-of-art method, on data acquired at sea. Once we have detected an object, we want to recognize it. For that, we use the correlation technique. The reference images have been obtained from 3D computer generated images of mines. This novel approach gives promising results. For each developed method, we have optimized the results through the use of navigational information. Indeed, depending on vehicle's motion, we can set constraints to improve the detection step and reduce processing time.BREST-SCD-Bib. electronique (290199901) / SudocSudocFranceF