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Recently, robust video transmission system that can eliminate the cliff effect in digital video transmission has attracted great interest from both academia and industry. By linearizing the whole system, robust video transmission is intrinsically scalable to channel conditions in mobile networks. However, the heterogeneity of user devices in terms of viewing resolution has not been well studied for robust video broadcast systems. In this paper, we propose a spatial scalability enabled robust video broadcast (SSRVB) system, aiming at accommodating diverse users with both heterogeneous resolutions and heterogeneous channel conditions. In SSRVB, a novel spatial decomposition method based on linear projection is first designed for robust video transmission. Then the transmission distortion minimization problem with joint subcarrier matching and power allocation is formulated. A near-optimal low-complexity subcarrier matching algorithm based on auction theory and an optimal power allocation strategy are also proposed. Furthermore, an iterative algorithm is designed to solve the problem of joint resource allocation. Simulation results demonstrate that SSRVB can achieve an average of 3 dB gain when compared with the reference schemes (i.e., ECast, MCast, discrete wavelet transform (DWT) based scheme, and scalable video coding (SVC) scheme) in terms of average peak signal-to-noise ratio under heterogeneous scenarios. [ABSTRACT FROM AUTHOR]

To cope with rapid growth of video services, we propose a non-orthogonal multiple access (NOMA) based scalable video multicast (NOMA-SVM) framework for mobile networks, by exploiting NOMA's specific potential in scalable video multicast transmission. We consider statistical channels, instead of channels with perfect estimation, in the proposed NOMA-SVM framework in order to capture the realistic channel behaviors. As quality of experience (QoE) is a better metric than throughput for video transmission, QoE-driven power allocation is performed among multiple video layers in the proposed NOMA-SVM framework, in which users can decode video with quality proportional to their channel conditions. Specifically, we formulate the power allocation problem with the goal to maximize the average QoE over all users while guaranteeing the basic services of these users. To solve such a non-convex discrete problem, an optimal algorithm is developed based on the hidden monotonicity of the problem. A suboptimal algorithm is also proposed with much lower complexity in order to meet the practical needs. Simulation results show that the proposed algorithms outperform existing orthogonal multiple access (OMA) and NOMA based algorithms under various multicast scenarios in terms of QoE. [ABSTRACT FROM AUTHOR]

Rate control typically involves two steps: bit allocation and bitrate control. The bit allocation step can be implemented in various fashions depending on how many levels of allocation are desired and whether or not an optimal rate–distortion (R-D) performance is pursued. The bitrate control step has a simple aim in achieving the target bitrate as precisely as possible. In our recent research, we have developed a \lambda -domain rate control algorithm that is capable of controlling the bitrate precisely for High Efficiency Video Coding (HEVC). The initial research showed that the bitrate control in the \lambda -domain can be more precise than the conventional schemes. However, the simple bit allocation scheme adopted in this initial research is unable to achieve an optimal R-D performance reflecting the inherent R-D characteristics governed by the video content. In order to achieve an optimal R-D performance, the bit allocation algorithms need to be developed taking into account the video content of a given sequence. The key issue in deriving the video-content-guided optimal bit allocation algorithm is to build a suitable R-D model to characterize the R-D behavior of the video content. In this paper, to complement the R- \lambda model developed in our initial work, a D- \lambda model is properly constructed to complete a comprehensive framework of \lambda -domain R-D analysis. Based on this comprehensive \lambda -domain R-D analysis framework, a suite of optimal bit allocation algorithms are developed. In particular, we design both picture-level and basic-unit-level bit allocation algorithms based on the fundamental R-D optimization theory to take full advantage of the content-guided principles. The proposed algorithms are implemented in HEVC reference software, and the experimental results demonstrate that they can achieve an obvious R-D performance improvement with a smaller bitrate control error. The proposed bit allocation algorithms have already been adopted by the Joint Collaborative Team on Video Coding and integrated into the HEVC reference software. [ABSTRACT FROM PUBLISHER]

Light field (LF) technology has been popularly adopted by a wide range of conventional industries. However, one problem when dealing with LFs is the sheer size of data volume. There have been many multi-view video coding (MVC)-based LF video coding methods reported in the literature, aiming at finding the best prediction structure for LF video coding. It is clear that the number of possible prediction structures is unlimited, and it is also observed that the coding bit-rate can be reduced by increasing the number of bi-directionally encoded views in the prediction structure. However, none work has been conducted to analyze the relationship of the prediction structure with its coding performance. In light of this observation, we first design a new LF-MVC prediction structure by extending the inter-view prediction into a two-directional parallel structure. Analytical models for source coding rate and encoding time are developed to analyze their relationships with the prediction structure, and are proven to be well-matched to our experimental results. Experimental evaluation of two LF video sequences demonstrates that the proposed LF-MVC prediction structure can achieve a factor of 26% bit-rate reduction against the conventional MVC prediction structure for an LF video with $5\times 5$ views, and a further 34% bit-rate reduction for an LF video with a larger $10\times 10$ views. Compared with the state-of-the-art MVC-based LF video coding prediction structures in the literature, LF-MVC can achieve the best coding performance, and with its high encoding efficiency, is well suited for deployment in practical LF-based 3D systems. [ABSTRACT FROM PUBLISHER]

The state-of-the-art High Efficiency Video Coding (HEVC) standard adopts a hierarchical coding structure to improve its coding efficiency. This allows for the quantization parameter cascading (QPC) scheme that assigns quantization parameters (Qps) to different hierarchical layers in order to further improve the rate-distortion (RD) performance. However, only static QPC schemes have been suggested in HEVC test model, which are unable to fully explore the potentials of QPC. In this paper, we propose an adaptive QPC scheme for an HEVC hierarchical structure to code natural video sequences characterized by diversified textures, motions, and encoder configurations. We formulate the adaptive QPC scheme as a non-linear programming problem and solve it in a scientifically sound way with a manageable low computational overhead. The proposed model addresses a generic Qp assignment problem of video coding. Therefore, it also applies to group-of-picture-level, frame-level and coding unit-level Qp assignments. Comprehensive experiments have demonstrated that the proposed QPC scheme is able to adapt quickly to different video contents and coding configurations while achieving noticeable RD performance enhancement over all static and adaptive QPC schemes under comparison as well as HEVC default frame-level rate control. We have also made valuable observations on the distributions of adaptive QPC sets in the videos of different types of contents, which provide useful insights on how to further improve static QPC schemes. [ABSTRACT FROM PUBLISHER]

We propose an adaptive hybrid digital–analog video transmission (A-HDAVT) scheme for robust video streaming in mobile networks with realistic fading channels. This scheme is fundamentally different from recent research in hybrid digital–analog video transmission in which wireless channels are unrealistically assumed to be Gaussian. For fading channels, it is critical to take full advantage of diversity in both video contents and multiuser channels. Like all hybrid approaches, A-HDAVT is designed to exploit the benefits from both digital and analog systems. To achieve this goal, each group of pictures is first transformed into one low-pass frame and several high-pass frames with motion-compensated temporal filtering. The critical low-pass frame is reliably transmitted as base layer in a digital mode, while high-pass frames are transmitted as enhancement layers in an analog mode to achieve desired graceful degradation performance. In analog transmission, we introduce a channel prediction-based adaptive power-distortion optimization (P-APDO) scheme to combat channel fading in mobile networks. The basic idea behind P-APDO is to perform power allocation based on the video content as well as the predicted channel status. Furthermore, we also investigate the multiuser scenarios in which the content diversity and channel diversity among users are appropriately exploited. Extensive simulations have been carried out to evaluate the performance of A-HDAVT under various degrees of channel fading. The results show that A-HDAVT achieves significant performance gains over competing schemes Robust Uncoded Video Transmission, Parcast, and Softcast in both single-user and multiuser scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, we investigate the problem of image contrast enhancement. Most existing relevant technologies often suffer from the drawback of excessive enhancement, thereby introducing noise/artifacts and changing visual attention regions. One frequently used solution is manual parameter tuning, which is, however, impractical for most applications since it is labor intensive and time consuming. In this research, we find that saliency preservation can help produce appropriately enhanced images, i.e., improved contrast without annoying artifacts. We therefore design an automatic contrast enhancement technology with a complete histogram modification framework and an automatic parameter selector. This framework combines the original image, its histogram equalized product, and its visually pleasing version created by a sigmoid transfer function that was developed in our recent work. Then, a visual quality judging criterion is developed based on the concept of saliency preservation, which assists the automatic parameters selection, and finally properly enhanced image can be generated accordingly. We test the proposed scheme on Kodak and Video Quality Experts Group databases, and compare with the classical histogram equalization technique and its variations as well as state-of-the-art contrast enhancement approaches. The experimental results demonstrate that our technique has superior saliency preservation ability and outstanding enhancement effect. [ABSTRACT FROM PUBLISHER]

We propose an improved coarse-grain scalable video coding (SVC) approach based on the structural similarity (SSIM) index as the visual quality criterion, aiming at maximizing the overall coding performance constrained by user-defined quality weightings for all scalable layers. First, we develop an interlayer rate-SSIM dependency model, by investigating bit rate and SSIM relationships between different layers. Second, a reduced-reference SSIM-Q model and a Laplacian R-Q model are introduced for SVC, by incorporating the characteristics of hierarchical prediction structure in each layer. Third, based on the user-defined weightings and the proposed models, we design a rate-distortion optimization approach to adaptively adjust Lagrange multipliers for all layers to maximize the overall rate-SSIM performance of the scalable encoder. Experiments with multiple layers, different layer weightings, and various videos demonstrate that the proposed framework can achieve better rate-SSIM performance than single layer optimization method, and provide better coding efficiency as compared to the conventional SVC scheme. Subjective tests further demonstrate the benefits of the proposed scheme. [ABSTRACT FROM AUTHOR]

Video transmission using Scalable Video Coding (SVC) enables the functionalities of adaptation for both channel bandwidth and terminal device. However, link adaptation, which requires the video bitstream to provide error resilient scalability (ERS), is currently not supported in SVC. In this paper, we propose a robust SVC bitstream transmission framework, which integrates both error resilient (ER) coding and error concealment (EC) to enable the desired link adaptability. First, at the encoder, we design an ER coding scheme for SVC using redundant pictures, in which redundant picture information (RPI) is generated under rate-distortion criteria and transmitted to the media gateway together with the SVC bitstream. Then, ERS can be effectively achieved at the media gateway by selectively adding or removing coded pictures of different video coding layers according to the RPI and current network link status. Finally, at the decoder, we take advantage of the unique characteristics of SVC to develop an EC scheme based on both the proposed Virtual-BLSkip method and Wiener filtering to recover the lost or removed enhancement layer pictures. The proposed framework has negligible impact on the coding efficiency because it transmits RPIs rather than directly adding redundancy into the original bitstream. More importantly, it successfully strikes a balance between coding efficiency, error resiliency, and operation complexity. It is capable of providing the desired ERS with extremely low complexity, and maintaining virtually the same bit rate as input video bitstream. The experimental results show that the proposed framework achieves significant performance gains over existing ER transmission schemes. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. [ABSTRACT FROM PUBLISHER]

This paper proposes a novel multi-level frame interpolation scheme by exploiting the interactions among different levels. The proposed scheme includes three major stages that work at block level, pixel level, and sequence level, respectively. Effective algorithms are designed for each stage, i.e., block-level motion estimation with dropping unreliable motion vectors, pixel-level motion vector-guided partial scale-invariant feature transform flow matching, and sequence-level 3-D total variation regularized completion. Compared to traditional methods that focus mostly at one single level, the proposed scheme manages to recognize and utilize the interactions among the three levels based on their distinct characteristics and intertwined relationships. With a proper exploitation of interactions, unique advantages for each level can be effectively preserved while inherent limitations of a given level can be overcome by utilizing information from other levels. Extensive experiments have confirmed its superior performance over several classical schemes, in both subjective visual quality and objective peak signal-to-noise ratio/structure similarity measurements, and typical artifacts can be significantly reduced. [ABSTRACT FROM AUTHOR]

For promising vision-based video coding on low-quality data, this paper proposes a sparse spatio-temporal representation with adaptive regularized dictionary learning and develops a low bit-rate video coding scheme. In a reversed-complexity Wyner–Ziv coding manner, it selects a subset of key frames to code at original resolution, while the rest are down sampled and reconstructed by a sparse spatio-temporal approximation using key frames as a training dataset. Since primitive patches (geometry) are of low dimensionality and can be well learned from the primitive patches across frames in a scale space, a video frame is divided into three layers: a primitive layer, a nonprimitive coarse layer, and a nonprimitive smooth layer. The multiscale differential feature representations are invertible to facilitate reconstruction with dictionary learning, and the target is formulated as an optimization problem by constructing a sparse representation of 2-D patches and 3-D volumes over adaptive regularized dictionaries, a set of 2-D subdictionary pairs trained from primitive patches, and a 3-D dictionary trained from nonprimitive volumes. Specifically, the nonprimitive layer is constructed as volumes in to order keep it consistent along the motion trajectory, which enables sparse representations over a learned 3-D spatio-temporal dictionary. Through hierarchical bidirectional motion estimation and adaptive overlapped block motion compensation, the 3-D low-frequency and high-frequency dictionary pair is designed by the K-SVD algorithm to update the atoms for optimal sparse representation and convergence. In reconstruction, the lost high-frequency information of the down-sampled frames can be synthesized from the sparse spatio-temporal representation over the adaptive regularized dictionaries. Extensive experiments validate the compression efficiency of the proposed scheme versus H.264/AVC in terms of both objective and subjective comparisons. [ABSTRACT FROM PUBLISHER]

Three-dimensional video (3DV) is attracting many interests with its enhanced viewing experience and more user driven features. 3DV has several unique characteristics different from 2D video: (1) It has a much larger amount of data captured and compressed, and corresponding video compression techniques can be much more complicated in order to explore data redundancy. This will lead to more constraints on users' network access and computational capability, (2) Most users only need part of the 3DV data at any given time, while the users' requirements exhibit large diversity, (3) Only a limited number of views are captured and transmitted for 3DV. View rendering is thus necessary to generate virtual views based on the received 3DV data. However, many terminal devices do not have the functionality to generate virtual views. To enable 3DV experience for the majority of users with limited capabilities, adaptive 3DV transmission is necessary to extract/generate the required data content and represent it with supported formats and bitrates for heterogeneous terminal devices. 3DV transcoding is an emerging and effective technique to achieve desired adaptive 3DV transmission. In this article, we propose the first efficient 3DV transcoding scheme that can obtain any desired view, either an encoded one or a virtual one, and compress it with more universal H.264/AVC. The key idea of the proposed scheme is to appropriately utilize motion information contained in the bitstream to generate candidate motion information. Original information of both the desired view and reference views are used to obtain this candidate information and a proper motion refinement process is carried out for certain blocks. Simulation results show that, compared to the straightforward cascade algorithm, the proposed scheme is able to output compressed bitstream of the required view with significantly reduced complexity while incurring negligible performance loss. Such a 3DV transcoding can be applied to most gateways that usually have constraints on computational complexity and time delay. [ABSTRACT FROM AUTHOR]

This paper proposes a distributed robust optimization scheme to jointly optimize overall video quality and traffic performance for scalable video multirate multicast over practical wireless networks. In order to guarantee layered utility maximization, the initial nominal joint source and network optimization is defined, where each scalable layer is tailored in an incremental order and finds jointly optimal multicast paths and associated rates with network coding. To enhance the robustness of the nominal convex optimization formulation with nonlinear constraints, we reserve partial bandwidth for backup paths disjoint from the primal paths. It considers the path-overlapping allocation of backup paths for different receivers to take advantage of network coding, and takes into account the robust multipath rate-control and bandwidth reservation problem for scalable video multicast streaming when possible link failures of primary paths exist. Specifically, an uncertainty set of the wireless medium capacity is introduced to represent the uncertain and time-varying property of parameters related to the wireless channel. The targeted uncertainty in the robust optimization problem is studied in a form of protection functions with nonlinear constraints, to analyze the tradeoff between robustness and distributedness. Using the dual decomposition and primal-dual update approach, we develop a fully decentralized algorithm with regard to communication overhead. Through extensive experimental results under critical performance factors, the proposed algorithm could converge to the optimal steady-state more quickly, and adapt the dynamic network changes in an optimal tradeoff between optimization performance and robustness than existing optimization schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose a generic error resilient video coding (ERVC) scheme using embedded Wyner–Ziv (WZ) description. At the encoder side, a joint source-channel R-D optimized mode selection (JSC-RDO-MS) algorithm with WZ-coded anchor frames is statistically studied and developed. Given a stationary first-order Markov Gaussian source, the proposed mode optimization is justified by an analysis of the RD impact on the WZ bit-rate. JSC-RDO-MS involves in the estimation of expected rate and distortion of WZ coding with the unavailable side information, and the WZ bit-rate of each coding mode is determined based on the error correction capability of the specific WZ codec. At the decoder side, an online correlation noise model between the source and the side-information is proposed with a mixture of Laplacians whose parameters are attained to reflect the coherence of the motion field of successive frames and the energy of prediction residual. Each mixture component represents the statistical distribution of prediction residuals, and the mixing coefficients represent the amount of errors in motion compensation. The proposed scheme achieves the so-called classification gain by exploiting the spatially non-stationary characteristics of the motion field and texture. Extensive experimental results show that the proposed WZ-ERVC scheme achieves a better overall RD performance than existing ERVC schemes, and the proposed modeling algorithm also significantly outperforms the conventional Laplacian model by up to 2 dB. [ABSTRACT FROM PUBLISHER]

This paper studies a joint coding/routing optimization between network lifetime and video distortion by applying information theory to wireless visual sensor networks for correlated sources. Arbitrary coding [distributed video coding and network coding (NC)] from both combinatorial optimization and information theory could make significant progress toward the performance limit and tractable. Also, multipath routing can spread energy utilization across nodes within the entire network to keep a potentially longer lifetime, and solve the wireless contention issues by the splitting traffic. The objective function not only keeps the total energy consumption of encoding power, transmission power, and reception power minimized, but ensures the information received by sink nodes to approximately reconstruct the visual field. Also, a generalized power consumption model for distributed video sources is developed, in which the coding complexity of Key frames and Wyner-Ziv frames is measured by translating specific coding behavior into energy consumption. On the basis of the distributed multiview video coding and NC-based multipath routing, the balance problem between lifetime (costs) and distortion (capacity) is modeled as an optimization formulation with a fully distributed solution. Through a primal decomposition, a two-level optimization is relaxed with Lagrangian dualization and solved by the gradient algorithm. The low-level optimization problem is further decomposed into a secondary master dual problem with four cross-layer subproblems: a rate control problem, a channel contention problem, a distortion control problem, and an energy conservation problem. The implementation of the distributed algorithm is discussed with regard to the communication overhead and dynamic network change. Simulation results validate the convergence and performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]