Anissa Mokraoui

Image quality assessment based on wave atoms transform

Image quality assessment is still an active field of research. The main objective of the developed image quality metric is to offer an index of quality that is consistent with the human subjective judgment of image quality. Despite the great number of developed metrics, there is still a need for image analysis tools that is able to extract the most perceptual relevant characteristics of an image. The goal of this work is then to propose a more advanced analysis and representation tools to extract more effective features that could be incorporated in the design of the image quality metric. In this paper, we propose a novel objective metric based on wave atoms transform. This new transform is half multiscale and half multi-directional. It offers a better representation of images containing oscillatory patterns and textures than the others known transforms [10]. In this work, we propose a new full reference image quality metric based on wave atom transform and exploiting some properties of the human visual system. The consistency of the proposed metric with subjective evaluation is performed on LIVE database. The obtained correlation of this metric with the MOS provided by the database is better than other known metrics confirming thus the efficiency of this new image quality measure in predicting image quality.

Wave atoms based compression method for fingerprint images

This paper proposes a new fingerprint image compression approach where the quality of the decoded image is perceptually controlled using wave atom transform. A comparative study of different transforms shows that wave atom transform is more appropriate than wavelets for fingerprint image compression. A new image quality metric based on the same transform is proposed to control the compression performance. Some properties of the human visual system are exploited in the design of the proposed image quality metric. Simulations show that this metric correlates well with the subjective human judgment. Based on these interesting results a compression method with a perceptual distortion control mechanism specific to fingerprint images is developed. Finally, the performance of the proposed compression scheme is evaluated in terms of fingerprint recognition rate. The experimental results clearly show that the proposed strategy offers better results than some traditional compression methods.

A new fingerprint image compression based on wave atoms transform

During the last decade the emergence of many transforms called geometric wavelets have attracted much attention of researchers working on image analysis. These new transforms propose a new representation richer than the classical wavelets multiscale representation. This paper presents a comparative study of these transforms in order to determine what is the best transform dedicated to a particular type of image that is the fingerprint image. While we are not trying to compete with the current standard JPEG2000, which is dedicated to all types of images, we are aware that for a particular type of images, we can do better by choosing for this type of specific image, a more appropriate tool than classical wavelets. The results show that for fingerprint images the wave atom offers better performance than the current transform based compression standard.

Sequential block-based disparity map estimation algorithm for stereoscopic image coding

This paper deals with the problem of block-based disparity map estimation for stereoscopic image coding where the estimated map is transmitted to the decoder in order to predict one view from the other. The estimation problem of the disparity map is thus a trade-off between the quality prediction and the binary cost of the disparities to be stored or transmitted. This trade-off is modeled as a joint entropy-distortion metric assuming that the disparity map is encoded with an entropy coder; and one of the two views is fully predicted using this map when applied to the other view. However minimizing this joint metric is a complex combinatorial optimization problem where choices of disparities are all interrelated. A sub-optimal optimization solution is then proposed. It is based on a tree structure which is constructed sequentially whenever a block is matched. The developed algorithm, called Modified M-Algorithm (MMA), processes the reference view in a raster scanning order and assumes that the disparities to be selected in the unprocessed area are likely to follow a chosen disparity distribution. This algorithm has the ability at each step of the process not only to retain the M-best paths of the tree in terms of entropy-distortion cost but also to explore all possible extensions of each of these M paths until reading the last block of the view. Simulations, conducted on stereoscopic images extracted from Middlebury and Deimos datasets, show the advantage of our MMA compared to the conventional Block Matching Algorithm (BMA) with and without regularization both in terms of reducing bitrate and distortion. HighlightsA block-based disparity map estimation algorithm for stereo image coding is proposed.The optimization algorithm relies on a joint entropy-distortion metric.A sub-optimal disparity map is built sequentially using a simplified tree structure.Comparisons are done with block-matching algorithms with and without regularization.Simulation results show significant gains in terms of rate-distortion.

Enhanced time synchronization in IEEE 802.11a system using SIGNAL field and MAP channel estimation

This paper addresses the problem of time synchronization in the IEEE 802.11a OFDM wireless communication system.In addition to the reference sequence usually used in conventional algorithms, recently the SIGNAL field of the physical packet has been exploited to improve the coarse time synchronization. To increase the performance of the fine time synchronization, we propose to use joint time synchronization and channel estimation based on the Maximum A Posteriori Probability criterion. Simulation results show that the probability of synchronization failure of the proposed synchronization algorithm is reduced as compared to existing methods.

Performance Bounds Analysis for Semi-Blind Channel Estimation in MIMO-OFDM Communications Systems

Most communications systems require channel estimation for equalization and symbol detection. Currently, this is achieved by using dedicated pilot symbols, which consume a non-negligible part of the throughput and power resources, especially for large dimensional systems. The main objective of this paper is to quantify the rate of reduction of this overhead due to the use of a semi-blind channel estimation. Different data models and different pilot design schemes have been considered in this paper. By using the Cramér Rao Bound (CRB) tool, the estimation error variance bounds of the pilot-based and semi-blind based channel estimators for a multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system are compared. In particular, for large MIMO-OFDM systems, a direct computation of the CRB is prohibitive, and hence, a dedicated numerical technique for its fast computation has been developed. Many key observations have been made from this comparative study. The most important one is that, thanks to the semi-blind approach, one can skip about 95% of the pilot samples without affecting the channel estimation quality.

Improved time synchronization in presence of imperfect channel state information

This paper addresses the time synchronization problem in IEEE 802.11a OFDM wireless systems. To enhance the coarse time synchronization mechanism, recent methods exploit not only traditional training sequences as specified by the standard but also additional knowledge available when the Carrier Sense Multiple Access with Collision Avoidance mechanism (CSMA/CA) is triggered. In this case, additional information can be used as training sequences based on the protocol knowledge training sequence known by the receiver. This step is followed by a time synchronization and channel estimation which results in the smallest Channel Estimate Errors (CEE) according to the selected criterion (e.g. LS, MAP). However it was found that the synchronization failure probability heavily depends on the channel estimate quality. Therefore to improve the performance of this class of algorithms, we propose an optimal time synchronization metric that minimizes the average of the transmission error over all CEE. Simulation results show a strongly improved performance in terms of synchronization failure probability in comparison with the existing algorithms.

Motion Estimation Algorithms Using the Deformation of Planar Hierarchical Mesh Grid for Videoconferencing Applications at Low Bit-rate Transmission

This paper studies the issue of reducing the temporal redundancy between consecutive frames of a videoconferencing sequence at low bit-rate transmission. To overcome the drawbacks of the traditional block matching algorithm implemented in the most current video coding standards, we propose to better describe the motion of objects through the deformation of planar rectangular mesh grid adapted to the edges of the moving objects in the scene. The traditional inter coding modes are then replaced by two new classes of encoding algorithms. The first one concerns the B-frames where the problem of motion estimation is solved by a bidirectional prediction algorithm which reconstructs the quadrilateral mesh grids without any coding cost. The second class of algorithm much more complex than the first one is specific to the P-frames based on the principle of merging two hierarchical grids of reference. This algorithm addresses not only the motion estimation problem based on the adaptive quadrilateral mesh grid but also the issue of the relevant information (e.g. the positions of the nodes, the connectivity of each quadrilateral mesh of the grid and the motion compensation) to efficiently encode. The implementation of these algorithms in a complete coding scheme offers good performance compared to the H.264/AVC video coder at low bit-rate transmission.

Robust Transmission of Compressed HTML Files over Wireless Channel using an Iterative Joint Source-Channel Decoding Receiver

This paper proposes an algorithm for the robust reception of compressed HTML files transmitted over a noisy mobile radio channel. Both source encoders and transmission systems are assumed to be standard compliant. The source encoder follows the HTTP1.1 protocol specifications, i.e. the HTML files are encoded by the deflate algorithm, a combination of Lempel-Ziv and Huffman algorithms. The transmission scheme follows IEEE 802.11a (and 802.11n) standard as an example. The proposed receiver is based on an iterative joint source-channel decoding approach. The Soft-Input Soft-Output outer source decoder is based on a sequential M-algorithm, which has been modified to improve the decoding performance by exploiting the specific grammatical and syntax rules of (i) Huffman codes; (ii) Lempel-Ziv codes; and (iii) HTML language. Simulation results following the IEEE 802.11a (and 802.11n) standard over additive white Gaussian noise and Rayleigh fading channels show that the proposed receiver drastically reduces the number of errors occurring in the received HTML files compared to the classical receivers. An EXIT chart analysis illustrates some properties of this combination of source and channel decoders.

Toward green communications using semi-blind channel estimation

In most Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems, channel estimation is required for equalization and symbol detection. It often exploits the specified pilot symbols consuming not only a large part of the throughput but also significant power resources. This paper quantifies the theoretical maximum power reduction of the transmitted pilots when semiblind channel estimator is deployed while ensuring the same pilot-based channel estimation performance for BPSK/QPSK data models and a block-type pilot arrangement as specified in the IEEE 802.11n standard. A Least Square Decision Feedback (LS-DF) semi-blind channel estimator is then considered showing that a reduction of 76% of the pilots power is obtained compared to the LS pilot-based estimator for the same channel estimation performance.