A parallel block-based encryption schema for digital images using reversible cellular automata

AbstractWe propose a novel images encryption schema based on reversible one-dimensional cellular automata. Contrasting to the sequential operating mode of several existing approaches, the proposed one is fully parallelizable since the encryption/decryption tasks can be executed using multiple processes running independently for the same single image. The parallelization is made possible by defining a new RCA-based construction of an extended pseudorandom permutation that takes a nonce as a supplementary parameter. The defined PRP exploit the chaotic behavior and the high initial condition's sensitivity of the RCAs to ensure perfect cryptographic security properties. Results of various experiments and analysis show that high security and execution performances can be achieved using the approach, and furthermore, it provides the ability to perform a selective area decryption since any part of the ciphered-image can be deciphered independently from others, which is very useful for real time applications

Robust and fast selective encryption for HEVC videos

Emerging High efficiency video coding (HEVC) is expected to be widely adopted in network applications for high definition devices and mobile terminals. Thus, construction of HEVC\u27s encryption schemes that maintain format compliance and bit rate of encrypted bitstream becomes an active security\u27s researches area. This paper presents a novel selective encryption technique for HEVC videos, based on enciphering the bins of selected Golomb–Rice code’s suffixes with the Advanced Encryption Standard (AES) in a CBC operating mode. The scheme preserves format compliance and size of the encrypted HEVC bitstream, and provides high visual degradation with optimized encryption space defined by selected Golomb–Rice suffixes. Experimental results show reliability and robustness of the proposed technique

Feature-based Color Correction of Multi-View Video for Coding and Rendering Enhancement

International audienceMulti-View Video (MVV) consists of capturing the same scene with multiple cameras from different viewpoints. Therefore, substantial illumination and color inconsistencies can be observed between the different views. These color mismatches can reduce significantly compression efficiency and rendering quality. In this paper, we propose a preprocessing method for cor- recting these color discrepancies in MVV. To consider 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 (SIFT), followed by RANSAC 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 to choose always by default the center view as the reference one, we propose an automatic selection algorithm based on both views statistics and quality. Experimental results show that the proposed method increases coding efficiency with gains of up to 1.1 dB and 2.2 dB for the luminance and chrominance components, respectively. Further, once the correction is performed, the color of real and rendered views is harmonized and looks very consistent as a whole

Asymmetric coding of stereoscopic 3D based on perceptual significance

10% best papers certificateInternational audienceAsymmetric 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

Asymmetric coding using Binocular Just Noticeable Difference and depth information for stereoscopic 3D

International audienceThe 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

Stereoscopic image quality metric based on local entropy and binocular just noticeable difference

10% best papers certificateInternational audienceDeveloping 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