Optimization of Probabilistic Shaping for Nonlinear Fiber Channels with Non-Gaussian Noise.

Probabilistic constellation shaping is investigated in the context of nonlinear fiber optic communication channels. Based on a general framework, different link types are considered-1. dispersion-managed channels, 2. unrepeatered transmission channels and 3. ideal distributed Raman amplified channels. These channels exhibit nonlinear effects to a degree that conventional probabilistic constellation shaping strategies for the additive white Gaussian (AWGN) noise channel are suboptimal. A channel-agnostic optimization strategy is used to optimize the constellation probability mass functions (PMFs) for the channels in use. Optimized PMFs are obtained, which balance the effects of additive amplified spontaneous emission noise and nonlinear interference. The obtained PMFs cannot be modeled by the conventional Maxwell-Boltzmann PMFs and outperform optimal choices of these in all the investigated channels. Suboptimal choices of constellation shapes are associated with increased nonlinear effects in the form of non-Gaussian noise. For dispersion-managed channels, a reach gain in 2 spans is seen and across the three channel types, gains of >0.1 bits/symbol over unshaped quadrature-amplitude modulation (QAM) are seen using channel-optimized probablistic shaping.

Compressive Online Robust Principal Component Analysis via - Minimization.

This paper considers online robust principal component analysis (RPCA) in time-varying decomposition problems such as video foreground-background separation. We propose a compressive online RPCA algorithm that decomposes recursively a sequence of data vectors (e.g., frames) into sparse and low-rank components. Different from conventional batch RPCA, which processes all the data directly, our approach considers a small set of measurements taken per data vector (frame). Moreover, our algorithm can incorporate multiple prior information from previous decomposed vectors via proposing an - minimization method. At each time instance, the algorithm recovers the sparse vector by solving the - minimization problem-which promotes not only the sparsity of the vector but also its correlation with multiple previously recovered sparse vectors-and, subsequently, updates the low-rank component using incremental singular value decomposition. We also establish theoretical bounds on the number of measurements required to guarantee successful compressive separation under the assumptions of static or slowly changing low-rank components. We evaluate the proposed algorithm using numerical experiments and online video foreground-background separation experiments. The experimental results show that the proposed method outperforms the existing methods.

Two-dimensional distributed-phase-reference protocol for quantum key distribution.

Quantum key distribution (QKD) and quantum communication enable the secure exchange of information between remote parties. Currently, the distributed-phase-reference (DPR) protocols, which are based on weak coherent pulses, are among the most practical solutions for long-range QKD. During the last 10 years, long-distance fiber-based DPR systems have been successfully demonstrated, although fundamental obstacles such as intrinsic channel losses limit their performance. Here, we introduce the first two-dimensional DPR-QKD protocol in which information is encoded in the time and phase of weak coherent pulses. The ability of extracting two bits of information per detection event, enables a higher secret key rate in specific realistic network scenarios. Moreover, despite the use of more dimensions, the proposed protocol remains simple, practical, and fully integrable.

Modeling the Quality of Videos Displayed With Local Dimming Backlight at Different Peak White and Ambient Light Levels.

This paper investigates the impact of ambient light and peak white (maximum brightness of a display) on the perceived quality of videos displayed using local backlight dimming. Two subjective tests providing quality evaluations are presented and analyzed. The analyses of variance show significant interactions of the factors peak white and ambient light with the perceived quality. Therefore, we proceed to predict the subjective quality grades with objective measures. The rendering of the frames on liquid crystal displays with light emitting diodes backlight at various ambient light and peak white levels is computed using a model of the display. Widely used objective quality metrics are applied based on the rendering models of the videos to predict the subjective evaluations. As these predictions are not satisfying, three machine learning methods are applied: partial least square regression, elastic net, and support vector regression. The elastic net method obtains the best prediction accuracy with a spearman rank order correlation coefficient of 0.71, and two features are identified as having a major influence on the visual quality.

Modeling the subjective quality of highly contrasted videos displayed on LCD with local backlight dimming.

Local backlight dimming is a technology aiming at both saving energy and improving visual quality on television sets. As the rendition of the image is specified locally, the numerical signal corresponding to the displayed image needs to be computed through a model of the display. This simulated signal can then be used as input to objective quality metrics. The focus of this paper is on determining which characteristics of locally backlit displays influence quality assessment. A subjective experiment assessing the quality of highly contrasted videos displayed with various local backlight-dimming algorithms is set up. Subjective results are then compared with both objective measures and objective quality metrics using different display models. The first analysis indicates that the most significant objective features are temporal variations, power consumption (probably representing leakage), and a contrast measure. The second analysis shows that modeling of leakage is necessary for objective quality assessment of sequences displayed with local backlight dimming.

Re-estimation of motion and reconstruction for distributed video coding.

Transform domain Wyner-Ziv (TDWZ) video coding is an efficient approach to distributed video coding (DVC), which provides low complexity encoding by exploiting the source statistics at the decoder side. The DVC coding efficiency depends mainly on side information and noise modeling. This paper proposes a motion re-estimation technique based on optical flow to improve side information and noise residual frames by taking partially decoded information into account. To improve noise modeling, a noise residual motion re-estimation technique is proposed. Residual motion compensation with motion updating is used to estimate a current residue based on previously decoded frames and correlation between estimated side information frames. In addition, a generalized reconstruction algorithm to optimize a multihypothesis reconstruction is proposed. The proposed techniques using motion and reconstruction re-estimation (MORE) are integrated in the SING TDWZ codec, which uses side information and noise learning. For Wyner-Ziv frames using GOP size 2, the MORE codec significantly improves the TDWZ coding efficiency with an average (Bjøntegaard) PSNR improvement of 2.5 dB and up to 6 dB improvement compared with DISCOVER.

Gigabit close-proximity wireless connections supported by 60 GHz RoF links with low carrier suppression.

We present an experimental investigation of the 60 GHz optical carrier suppressed radio over fiber systems with less than 5 dB carrier suppression. As a case study, the 60 GHz RoF signal is generated using a 12.5 Gb/s commercially available Mach-Zehnder modulator biased at its minimum point. We report on error free transmission over 20 km of standard single mode fiber and 1 m of wireless distance. Furthermore, the efficiency of photonic RF generation depending on the value of carrier suppression is reported. We argue that transport of RoF signals with low carrier suppression assisted with simplified techniques of lightwave generation, baseband data modulation, and RF downconversion might be a promising enabling technology for fiber support of close-proximity wireless terminals.

A novel method for combating dispersion induced power fading in dispersion compensating fiber.

We experimentally investigate the performance of 60 GHz double sideband (DSB) radio over fiber (RoF) links that employ dispersion compensating fiber (DCF). Error free transmission of 3 Gbps signals over 1 m of wireless distance is reported. In order to overcome experimentally observed chromatic dispersion (CD) induced power fading of radio frequency (RF) signal, we propose a method for improvement of RF carrier-to-noise (C/N) ratio through introduction of a degree of RF frequency tunability. Overall results improve important aspects of directly modulated RoF systems and demonstrate the feasibility of high carrier frequency and wide bandwidth RF signals delivery in RoF links including DCF fiber. Error free performance that we obtain for 3 Gbps amplitude shift-keying (ASK) signals enables uncompressed high-definition 1080p video delivery.

Optimal local dimming for LC image formation with controllable backlighting.

Light emitting diode (LED)-backlit liquid crystal displays (LCDs) hold the promise of improving image quality while reducing the energy consumption with signal-dependent local dimming. However, most existing local dimming algorithms are mostly motivated by simple implementation, and they often lack concern for visual quality. To fully realize the potential of LED-backlit LCDs and reduce the artifacts that often occur in current systems, we propose a novel local dimming technique that can achieve the theoretical highest fidelity of intensity reproduction in either l(1) or l(2) metrics. Both the exact and fast approximate versions of the optimal local dimming algorithm are proposed. Simulation results demonstrate superior performances of the proposed algorithm in terms of visual quality and power consumption.

Side information and noise learning for distributed video coding using optical flow and clustering.

Distributed video coding (DVC) is a coding paradigm that exploits the source statistics at the decoder side to reduce the complexity at the encoder. The coding efficiency of DVC critically depends on the quality of side information generation and accuracy of noise modeling. This paper considers transform domain Wyner-Ziv (TDWZ) coding and proposes using optical flow to improve side information generation and clustering to improve the noise modeling. The optical flow technique is exploited at the decoder side to compensate for weaknesses of block-based methods, when using motion-compensation to generate side information frames. Clustering is introduced to capture cross band correlation and increase local adaptivity in the noise modeling. This paper also proposes techniques to learn from previously decoded WZ frames. Different techniques are combined by calculating a number of candidate soft side information for low density parity check accumulate decoding. The proposed decoder side techniques for side information and noise learning (SING) are integrated in a TDWZ scheme. On test sequences, the proposed SING codec robustly improves the coding efficiency of TDWZ DVC. For WZ frames using a GOP size of 2, up to 4-dB improvement or an average (Bjøntegaard) bit-rate savings of 37% is achieved compared with DISCOVER.

Optimization of high-definition video coding and hybrid fiber-wireless transmission in the 60 GHz band.

The paper addresses the problem of distribution of high-definition video over fiber-wireless networks. The physical layer architecture with the low complexity envelope detection solution is investigated. We present both experimental studies and simulation of high quality high-definition compressed video transmission over 60 GHz fiber-wireless link. Using advanced video coding we satisfy low complexity and low delay constraints, meanwhile preserving the superb video quality after significantly extended wireless distance.

Efficient coding of shape and transparency for video objects.

A novel scheme for coding gray-level alpha planes in object-based video is presented. Gray-level alpha planes convey the shape and the transparency information, which are required for smooth composition of video objects. The algorithm proposed is based on the segmentation of the alpha plane in three layers: binary shape layer, opaque layer, and intermediate layer. Thus, the latter two layers replace the single transparency layer of MPEG-4 Part 2. Different encoding schemes are specifically designed for each layer, utilizing cross-layer correlations to reduce the bit rate. First, the binary shape layer is processed by a novel video shape coder. In intra mode, the DSLSC binary image coder presented in [3] is used. This is extended here with an intermode utilizing temporal redundancies in shape image sequences. Then the opaque layer is compressed by a newly designed scheme which models the strong correlation with the binary shape layer by morphological erosion operations. Finally, three solutions are proposed for coding the intermediate layer. The knowledge of the two previously encoded layers is utilized in order to increase compression efficiency. Experimental results are reported demonstrating that the proposed techniques provide substantial bit rate savings coding shape and transparency when compared to the tools adopted in MPEG-4 Part 2.

Context-based coding of bilevel images enhanced by digital straight line analysis.

A new efficient compression scheme for bilevel images containing locally straight edges is presented. This paper is especially focused on lossless (intra) coding of binary shapes for image and video objects, but other images with similar characteristics such as line drawings, layers of digital maps, or segmentation maps are also encoded efficiently. The algorithm is not targeted at document images with text, which can be coded efficiently with dictionary-based techniques as in JBIG2. The scheme is based on a local analysis of the digital straightness of the causal part of the object boundary, which is used in the context definition for arithmetic encoding. Tested on individual images of standard TV resolution binary shapes and the binary layers of a digital map, the proposed algorithm outperforms PWC, JBIG, JBIG2, and MPEG-4 CAE. On the binary shapes, the code lengths are reduced by 21%, 27%, 28%, and 41%, respectively. On the map layers, the reductions are 31%, 34%, 32%, and 64%, respectively. The algorithm is also more efficient on the test material than the state-of-the-art generic bilevel image coder free tree.

Optimal context quantization in lossless compression of image data sequences.

In image compression context-based entropy coding is commonly used. A critical issue to the performance of context-based image coding is how to resolve the conflict of a desire for large templates to model high-order statistic dependency of the pixels and the problem of context dilution due to insufficient sample statistics of a given input image. We consider the problem of finding the optimal quantizer Q that quantizes the K-dimensional causal context Ct = (Xt-t1,Xt-t2,...,X t-tK) of a source symbol Xt into one of a set of conditioning states. The optimality of context quantization is defined to be the minimum static or minimum adaptive code length of given a data set. For a binary source alphabet an optimal context quantizer can be computed exactly by a fast dynamic programming algorithm. Faster approximation solutions are also proposed. In case of m-ary source alphabet a random variable can be decomposed into a sequence of binary decisions, each of which is coded using optimal context quantization designed for the corresponding binary random variable. This optimized coding scheme is applied to digital maps and alpha-plane sequences. The proposed optimal context quantization technique can also be used to establish a lower bound on the achievable code length, and hence is a useful tool to evaluate the performance of existing heuristic context quantizers.

Content layer progressive coding of digital maps.

A new lossless context based method is presented for content progressive coding of limited bits/pixel images, such as maps, company logos, etc., common on the World Wide Web. Progressive encoding is achieved by encoding the image in content layers based on color level or other predefined information. Information from already coded layers are used when coding subsequent layers. This approach is combined with efficient template based context bilevel coding, context collapsing methods for multilevel images and arithmetic coding. Relative pixel patterns are used to collapse contexts. Expressions for calculating the resulting number of contexts are given. The new methods outperform existing schemes coding digital maps and in addition provide progressive coding. Compared to the state-of-the-art PWC coder, the compressed size is reduced to 50-70% on our layered map test images.