Atef Masmoudi

Efficient adaptive arithmetic coding based on updated probability distribution for lossless image compression

We propose an efficient lossless compression scheme for still images based on arithmetic coding. The scheme presents a novel adaptive arithmetic coding that updates the probabilities of pixels only after detecting the last occurrence of each pixel and then removes the redundancy from the original image effectively. The proposed approach has interestingly low computational complexity. In addition, unlike other statistical coding techniques, arithmetic cod- ing in the proposed scheme is not solely dependent on the pixel probability distribution but also on the image block sorting. The pro- posed method is compared to both static and adaptive order-0 mod- els while taking into account compression ratios and processing time. Experimental results, based on a set of 100 gray-level images, demonstrate that the proposed scheme gives mean compression ratios that are 5.5% higher than those by the conventional arithmetic encoders as well as significantly faster than the order-0 adaptive arithmetic coding.

Lossless chaos-based crypto-compression scheme for image protection

In this study, the authors proposed a new scheme which performs both lossless compression and encryption of images. Lossless compression is done by arithmetic coding (AC) while encryption is based on a chaos-based pseudorandom bit generator. Hence, they proposed to incorporate recent results of chaos theory into AC in order to shuffle the cumulative frequency vector of input symbols chaotically to make AC secure and the decoding process completely key-dependent. Many other techniques based on varying the statistical model used by AC have been proposed in literature, however, these techniques suffer from losses in compression efficiency that result from changes in entropy model statistics and are weak against known attacks. The proposed compression–encryption techniques were developed and discussed. The numerical simulation analysis indicates that the proposed scheme is highly satisfactory for image encryption without any AC compression efficiency loss. In addition, it can be incorporated into any image compression standard or algorithm employing AC as entropy coding stage, including static, adaptive and context-based adaptive models, and at any level, including bit, pixel and predictive error pixel levels.

An Efficient PRBG Based on Chaotic Map and Engel Continued Fractions

In recent years, a variety of chaos-based cryptosystems have been proposed. Some of these systems are used in designing a pseudo random bit generator (PRBG) for stream cipher applications. Most of the chaotic systems used in cryptography have good chaotic properties like ergodicity, sensitivity to initial values and sensitivity to control parameters. However, some of them are not very suitable for use in cryptography because of their non-uniform density function, and their relatively small key space. To be used in cryptography, a PRBG may need to meet stronger requirements than for other applications. In particular, various statistical tests can be applied to the outputs of such generators to conclude whether the generator produces a truly random sequence or not. In this paper, we propose a PRBG based on the use of the standard chaotic map with large key space and the Engle Continued Fractions (ECF) map. The outputs of the standard map are used as the inputs of ECF-map. The chaotic nature of the standard map and the good statistical properties of the ECF map motivate us to design a new PRBG for stream cipher applications. The numerical simulation analysis indicates that our PRBG produces bit sequences possessing excellent statistical and cryptographic properties.

Image encryption using chaotic standard map and engle continued fractions map

Recently, a variety of chaos-based cryptosystems have been proposed. Some of these novel chaotic encryption schemes are not very suitable for image encryption because of their density function which is not uniformly distributed or because of their small key space. Note that for some chaotic map, their control parameters and initial values can be found easily. In this paper, we propose a new scheme for image encryption based on the use of the standard chaotic map with large key space and Engle Continued Fractions (ECF) map. The ECF-map is employed to generate a pseudo random sequence which satisfies uniform distribution, zero co-correlation and ideal nonlinearity to achieve higher level of security. The proposed scheme is resistant to statistic attacks, differential attacks and some other known attacks. Theoretic and numerical simulation analyses on 100 images indicate that our scheme is efficient and satisfies high security.

An Adaptive Block-Based Histogram Packing for Improving the Compression Performance of JPEG-LS for Images with Sparse and Locally Sparse Histograms

In this paper, we propose a new adaptive block-based histogram packing for lossless compression of images with sparse and locally sparse histograms. JPEG-LS provides an efficient lossless compression at a reasonable complexity. However, its efficiency is severely affected when encoding images or blocks containing only a subset of the possible values from the nominal alphabet. The aim of this work is to improve the compression performance of JPEG-LS by introducing a preprocessing technique that leads to improvements. Results of its effectiveness are presented.

Secure chaotic dual encryption scheme for H.264/AVC video conferencing protection

This study presents a chaos-based crypto-compression scheme to protect the H.264 advanced video coding (AVC) used for the applications of video conference. To this end, a selective encryption approach was adopted. The authors proposed to encrypt the inter-prediction, the intra-prediction and the context adaptive variable length coding. The format compliance and compression ratio are conserved. Their encryption method is based on two piecewise linear chaotic maps for pseudo-random bit generators. The first is considered as a decision module to choose either to encrypt or not, whereas the second is used for the encryption. The proposed encryption techniques were developed and discussed, and their experimental results indicate that the scheme is secure and very efficient according to the computing times. Moreover, it is suitable for real-time application and convenient for all the H.264/AVC profiles or even with other video compression standards.

A preprocessing technique for improving the compression performance of JPEG 2000 for images with sparse or locally sparse histograms

JPEG 2000 is one of the most efficient and well performing standards for continuous-tone natural images compression. However, a compression performance loss may occur when encoding images with sparse or locally sparse histograms. Images of the later type include only a subset of the available intensity values implied by the nominal alphabet. This article proposes a new adaptive block-based histogram packing which improves the lossless compression performance of JPEG 2000 with sparse histogram images. We take advantage, in this work, of the strength likelihood between symbol sets of the neighboring image blocks and the efficiency of the offline histogram packing with sparse or locally sparse histogram images. Results of its effectiveness with JPEG 2000 are presented.

Outlier Detection in Wireless Sensor Networks Based on OPTICS Method for Events and Errors Identification

Wireless Sensor Network is composed of small, low cost, low energy, and multifunctional sensors. In addition, this network could have scalability, topology, synchronization, radio-coverage, safety and security constraints . Therefore, our challenge is to classify data into normal and abnormal measurements using outlier detection methods. This paper explore the density-based method Ordering Points to Identify the Clustering Structure. Proposed detector applies an auto- configuration of parameters without previous known environmental conditions. It also extracts hierarchical clusters that serve in a post-processing treatment for classification of data into errors and events. Performance is examined within a real and synthetic databases from Intel Berkeley Research lab. Results demonstrate that our proposed process analyzes data of this network with an average equal to 81% of outlier detection rate, 74% of precision rate and only 2% of false alarms rate that it is very low compared to other methods.

Iris Localization Using Mixture of Gamma Distributions in the Segmentation Process

This paper contributes to more accurate iris segmentation. We propose a new approach for iris image segmentation based on mixture of Gamma distributions modeling and an extended Expectation Maximization (EM) algorithm. We apply our approach to segment iris images from the CASIA (Chinese Academy of Sciences Institute of Automation)-Iris-Twins testing database. The accuracy of our algorithm is proved based on Kullback Leibler distance computation.