Threshold multi-secrets sharing schemes allow sharing a set of m secrets among n participants, while secrets can be revealed only if t or more participants collude. Although many multi-secret sharing schemes have been proposed, several improvements remain essential in order to cope with actual effectiveness and security requirements, including computational performances and compliance for large-scale data. In this paper, we present a novel multi-secrets (m, t, n)-threshold scheme using overdetermined systems of linear equations defined over finite Galois fields. The scheme provides unconditional security, linear sharing/reconstructing complexities and holds secure verifiability and t-consistence. By considering both secrets and shares as elements over finite Galois fields GF(2r), optimal and space-efficient representation is ensured compared to recent sharing schemes. In addition, the scheme provides dynamic secrets sharing, forgery/cheating detection and robustness against common attacks, while lower computational overhead is required.

The problem of determining the best level of asymmetry has been addressed by several recent works with the aim to guarantee an optimal binocular perception while keeping the minimum required information. To do so, subjective experiments have been conducted for the definition of an appropriate threshold. However, such an approach is lacking in terms of generalization because of the content variability. Moreover, using a fixed threshold does not allow an adaptation to the content and to the images' quality. The traditional asymmetric stereoscopic coding methods apply a uniform asymmetry by considering that all regions of an image have the same perceptual relevance which is not in compliance with the characteristics of human visual system (HVS). Consequently, this paper describes a fully automated model that dynamically determines the best bounds of asymmetry for each region of the image. Based on the Binocular Just Noticeable Difference (BJND) and the depth level in the scene, the proposed method achieves non-uniform reduction of spatial resolution of one view of the stereo pair with the aim to reduce bandwidth requirement. Experimental results show that the proposed method results in up to 43% of bitrate saving while outperforming the widely used asymmetric coding approaches in terms of 3D visual quality.

Developing a metric that can reliably predict the perceptual 3D quality as perceived by the end user, is a challenging issue and a necessary tool for the success of 3D multimedia applications. The various attempts at predicting 3D quality of experience as the combination of 2D quality of the left and right images have shown their limitations, and particularly for the case of asymmetric distortions. In this paper we propose a full reference quality assessment metric for stereoscopic images based on the perceptual binocular characteristics. The proposed metric handles effectively the asymmetric distortions of stereoscopic images, by incorporating human visual system (HVS) characteristics. Our approach was motivated by the fact that in case of asymmetric quality, 3D perception mechanisms supports the view providing the most important and contrasted information. To achieve that, weighting factors are defined for the quality of each view according to the local information content. Add to that, to take into account the sensitivity of the HVS, quality score of each region are modulated based on the Binocular Just Noticeable Difference (BJND). Experimental results show that the proposed metric correlates better with human perception than the state-of-the-art metrics.

Asymmetric stereoscopic coding is a very promising technique to decrease the bandwidth required for stereoscopic 3D delivery. However, one large obstacle is linked to the limit of asymmetric coding or the just noticeable threshold of asymmetry, so that 3D viewing experience is not altered. By way of subjective experiments, recent works have attempted to identify this asymmetry threshold. However, fixed threshold, highly dependent on the experiment design, do not allow to adapt to quality and content variation of the image. In this paper, we propose a new non-uniform asymmetric stereoscopic coding adjusting in a dynamic manner the level of asymmetry for each image region to ensure unaltered binocular perception. This is achieved by exploiting several HVS-inspired models; specifically we used the Binocular Just Noticeable Difference (BJND) combined with visual saliency map and depth information to quantify precisely the asymmetry threshold. Simulation results show that the proposed method results in up to 44% of bitrate saving and provides better 3D visual quality compared to state-of-the-art asymmetric coding methods.

Multiview video (MVV) consists of capturing the same scene with multiple cameras from different viewpoints. Therefore, substantial illumination and color inconsistencies can be observed between different views. These color mismatches can significantly reduce compression efficiency and rendering quality. In this paper, we propose a preprocessing method for correcting these color discrepancies in MVV. To consider the occlusion problem, our method is based on an improvement of histogram matching (HM) algorithm using only common regions across views. These regions are defined by an invariant feature detector (scale invariant feature transform), followed by random sample consensus algorithm to increase the matching robustness. In addition, to maintain temporal correlation, HM algorithm is applied on a temporal sliding window, allowing to cope with time-varying acquiring system, camera moving capture, and real-time broadcasting. Moreover, unlike always choosing the center view as the reference by default, we propose an automatic selection algorithm based on both views statistics and quality. The experimental results show that the proposed method increases coding efficiency with gains of up to 1.1 and 2.2 dB for the luminance and chrominance components, respectively. Furthermore, once the correction is performed, the color of real and rendered views is harmonized and looks very consistent as a whole.

The present paper describes a new approach of classification using genetic programming. The proposed technique consists of genetically co-evolve a population of nonlinear transformations on the input data to be classified, and map them to a new space with reduced dimension in order to get a maximum inter-classes discrimination. It is much easier to classify the new samples from the transformed data. Contrary to the existing GP-classification techniques, the proposed one uses a dynamic repartition of the transformed data in separated intervals, the efficiency of a given intervals repartition is handled by the fitness criterion, with a maximum classes discrimination. Experiments were performed using the Fisher's Iris dataset. After that, the KDD'99 Cup dataset was used to study the intrusion detection and classification problem. The results demonstrate that the proposed genetic approach outperforms the existing GP-classification methods, and provides improved results compared to other existing techniques.