Search

In multi-person videos, especially team sport videos, a semantic event is usually represented as a confrontation between two teams of players, which can be represented as collective motion. In broadcast basketball videos, specific camera motions are used to present specific events. Therefore, a semantic event in broadcast basketball videos is closely related to both the global motion (camera motion) and the collective motion. A semantic event in basketball videos can be generally divided into three stages: pre-event, event occurrence (event-occ), and post-event. By analyzing the influence of different stages of video segments to semantic events discrimination, it is observed that the pre-event and event-occ segments are effective for classification, while the post-events are effective for event success/failure classification. In this paper, we propose an ontology-based global and collective motion pattern (On_GCMP) algorithm for the basketball event classification. First, a two-stage GCMP-based event classification scheme is proposed. The GCMP is extracted using the optical flow. The two-stage scheme progressively combines a five-class event classification algorithm on event-occs and a two-class event classification algorithm on pre-events. Both algorithms utilize the sequential convolutional neural networks (CNNs) and the long short-term memory (LSTM) networks to extract the spatial and temporal features of GCMP for event classification. Second, we utilize the post-event segments to predict success/failure using deep features of images in the video frames (RGB_DF_VF)-based algorithms. Finally, the event classification results and success/failure classification results are integrated to obtain the final results. To evaluate the proposed scheme, we collected a new dataset called NCAA+, which is automatically obtained from the NCAA dataset by extending the fixed length of video clips forward and backward of the corresponding semantic events. The experimental results demonstrate that the proposed scheme achieves the mean average precision of 58.10% on NCAA+. It is higher by 6.50% than the state of the art on NCAA. [ABSTRACT FROM AUTHOR]

Video delivery has been playing an essential role in video services over edge networks. Although HTTP segment-based streaming, e.g., Dynamic Adaptive Streaming over HTTP (DASH), has become the prevailing technique, it cannot provide guaranteed video playback in terms of bitrate to mobile users. In essence, HTTP streaming downloads the video segments in a best effort fashion, i.e., passively responding to the channel dynamics. This can cause unstable playback with frequent rebuffer and multi-client competition that degrades a network-wide performance. In this paper, we present a network-assisted streaming framework for Guaranteed Playback-Experience Streaming over HTTP (GESH) that leverages the proactive control of network resources and joint coordination among multiple clients for service-aware network resource allocation. Specifically, GESH is empowered by a new weighted proportional fair scheduling without modifying existing cellular infrastructure, a per-segment channel variation model, and a suite of algorithms to seek the optimal weights for the scheduling. Extensive evaluations show that GESH can maximally guarantee the video playback of multiple users, as well as significantly outperforming conventional HTTP streaming and current DASH systems. [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]

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies. [ABSTRACT FROM PUBLISHER]

Cloud gaming has emerged as a promising application to enable high-end game playing with thin clients. Transmission control protocol (TCP) is pervasively adopted as the transport-layer protocol in the mainstream cloud gaming systems for video communication. However, streaming mobile cloud gaming video using the TCP is challenged with several key technical barriers: 1) the performance limitations of wireless networks in bandwidth and reliability; 2) the high throughput demand and stringent delay constraint imposed by high-quality gaming video transmission; and 3) the deadline violations and throughput fluctuations caused by the packet retransmission and congestion control mechanisms in the TCP. To address these critical problems, this paper proposes an application-layer source–forward error correction (FEC) coding framework dubbed adaptive source–FEC coding over TCP (ESCOT). First, we analytically formulate the optimization problem of joint source–FEC coding to minimize the end-to-end distortion of real-time video communication over TCP. Second, we develop a heuristic solution for effective loss rate approximation, source rate control, and FEC coding adaptation. ESCOT is distinct from existing source–FEC coding schemes in proactively analyzing and leveraging the TCP characteristics. The proposed solution is able to effectively mitigate both consecutive and sporadic video frame drops caused by congestion and random packet losses. We conduct the performance evaluation through extensive emulations in the Exata platform using real-time gaming video encoded by the H.264 codec. Experimental results show that the ESCOT advances the state of the art with noticeable improvements in video peak signal-to-noise ratio, end-to-end delay, goodput, and frame success rate. [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 contemporary multiuser multiple-input multiple-output systems, it is common for the reception devices to have a varying number of antennas. When multicast is performed, the number of concurrent spatial streams is limited by the device with the least number of antennas, which prevents more capable devices from getting higher rates. In this paper, we address the antenna heterogeneity in wireless video multicast by the innovative design of multiple similar description (MSD) video coding and multiplexed space–time block coding (M-STBC). MSD coding generates multiple descriptions of a video and features that any linear combinations of the descriptions are decodable. The descriptions comprising of real numbers are further processed by transform and power allocation steps for efficient transmission in a power-constrained system. M-STBC puts symbols in similar descriptions to corresponding space–time positions and ensures decodability under any antenna settings and channel conditions. As a result, we build up a scalable video multicast system, named AirScale, which allows receivers with a various number of antennas to decode from a single transmission, and the reconstructed video quality improves with the number of equipped antennas. Evaluations on Sora shows that, in a \1,2,3,4\ \times 4 system, AirScale provides baseline quality for one-antenna receiver and a much higher quality for multiantenna receivers. The gain over SoftCast is up to 3.5, 3.9, and 4.1 dB for two-, three-, and four-antenna receivers, respectively. [ABSTRACT FROM PUBLISHER]

Reconstructing a full-resolution stereoscopic video from an asymmetric stereoscopic video is a challenging task. The existing approaches require depth information, which imposes an additional challenge in data acquisition. In this paper, we propose a novel scheme that is capable of obtaining super-resolution and depth estimation simultaneously from an asymmetric stereoscopic video. The proposed scheme models the video super-resolution and stereo matching with a unified energy function. Then, we apply an alternating optimization method to minimize this energy function, which can be implemented with a two-step algorithm. In the first step we calculate the initial depth map by using a region-based cooperative optimization technique while considering the temporal consistency in video. In the second step we resolve the super-resolution problem under the guidance of the depth information. It is effective because each step benefits from the additional improvement over the previous step. We iteratively update the two steps until stable depth and super-resolution results are obtained. We have conducted a series of experiments on public stereoscopic video sequences to evaluate the performance of the proposed method. Both objective indexes and subjective visual comparisons verify that the proposed scheme can achieve satisfactory super-resolution results and high-quality depth map simultaneously. In particular, the subjective evaluation experiments on a 3-D monitor show that this scheme outperforms others and achieves the best visual sharpness. [ABSTRACT FROM AUTHOR]

High-frame-rate (HFR) video is emerging in popular gaming applications to enhance the smooth experience perceived by end users. However, it is challenging to guarantee the delivery quality of HFR video in mobile cloud gaming scenarios because of the high transmission rate and limited wireless resources. To address this critical problem, we develop a novel transmission scheduling framework dubbed AdaPtive HFR vIdeo Streaming (APHIS). The term adaptive indicates this scheme’s capability in dynamically adjusting the video traffic load and forward error correction (FEC) coding. First, we propose an online video frame selection algorithm to minimize the total distortion based on the network status, input video data, and delay constraint. Second, we introduce an unequal FEC coding scheme to provide differentiated protection for Intra (I) and Predicted (P) frames with low-latency cost. The proposed APHIS framework is able to appropriately filter video frames and adjust data protection levels to optimize the quality of HFR video streaming. We conduct extensive emulations in Exata involving HFR video encoded with H.264 codec. Experimental results show that APHIS outperforms the reference transmission schemes in terms of video peak signal-to-noise ratio, end-to-end delay, and goodput. Therefore, we recommend APHIS for delivering HFR video streaming in mobile cloud gaming systems. [ABSTRACT FROM AUTHOR]

This paper presents a novel smart downlink scheduling scheme to enhance the performance of multimedia transmission over long-term evolution (LTE) networks. LTE represents a promising framework for the next-generation broadband multimedia services because of its significantly increased data rate over 3G cellular networks. However, the current LTE scheduling scheme has largely been designed for general data traffic without adequate consideration of multimedia characteristics. Moreover, the hard handoff (HO) procedure adopted in LTE will further degrade the multimedia services when the mobile user moves from one cell to another. Even with increased data rate, current LTE systems still cannot deliver the expected quality of service (QoS) to the mobile users under various mobility scenarios, especially when the hard HO is evoked. Aiming at overcoming these major challenges, we develop in this paper a QoS-driven smart downlink scheduling scheme for enhanced multimedia transmission over LTE networks. The proposed design shall take the following QoS metrics into consideration: 1) the delay constraint of voice-over-Internet protocol flow; 2) the packet deadline of video flow; and 3) the service degradation induced by the hard HO procedure. We achieve the design objectives by creating three QoS-driven operational control modules: 1) a transmission delay control module to ensure the on-time arrival of various types of multimedia data; 2) an HO control module to warrant continuous multimedia services when the user moves across a cell boundary; and 3) a resource allocation module to strategically map the requested flows to best fit radio resource blocks. The simulation results confirm the performance gain of the proposed scheme. [ABSTRACT FROM AUTHOR]

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]

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]

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]

This paper presents a new video compression framework over H.264/AVC scheme that integrates our proposed structured priority belief propagation (BP)-based inpainting prediction (IP) to exploit the intrinsic nonlocal and geometric regularity in video samples. Unlike the existing edge-based inpainting adopted in lossy image coding, the optimal predictor could maintain the pixel-wise fidelity and the robust error resilience without any assistant information. Beyond the local prediction limitation of traditional intra and inter-modes, the priority BP with regularized structure priors of a spatio-temporal Markov random field is imposed on the predictor in an adaptive and more convergent sense. Specifically, the structured sparsity of the predicted macroblock region is inferred by tensor voting projected from the co-located decoded regions. In turn, the priority and visiting order of nodes are assigned according to the sets of updated beliefs as the propagation of messages. Through relatively few iterations of forward and backward process, the sparse inference of priority BP would ensure a stable marginal belief distribution on the structure and texture through updating local messages and beliefs. Within the optimal mode selection on rate-distortion optimization (RDO), the IP-mode with structured priority BP outperforms the existing vision-based approaches, and specially achieves a better objective rate-distortion performance besides visual quality. The IP-mode with structured priority BP can be applied to both I and P frames to generate low entropy residue, e.g., homogeneous visual patterns, and the computation complexity is also competitive with one iteration of sparse inference. Moreover, it behaves more resilient with an intrinsic probabilistic inference than the intra and inter-modes. [ABSTRACT FROM AUTHOR]

Within the existing reconstruction process of distributed video coding (DVC), there are two major approaches: the maximum probability reconstruction and the minimum mean square error (MMSE) reconstruction. Both of them assume that each node, a pixel in pixel domain DVC or a coefficient in transform domain DVC, is i.i.d., and reconstruct the value of each node independently by only exploiting statistical correlation between source and side-information. These kinds of models produce considerable amount of artifacts in decoded Wyner–Ziv (WZ) frames and degrade the objective performance. In this paper, we propose a context-adaptive Markov random field (MRF) reconstruction algorithm which exploits both the statistical correlation and the spatio-temporal consistency by modeling the corresponding MRF of a generic DVC architecture, and solve the inference by finding its MRF-based maximum a posteriori (MAP) estimate. The energy function of the MRF model consists of two terms: a data term measuring the statistical correlation, and a geometric regularity term enforcing local spatio-temporal structure consistency which is modeled by optical flow estimation with regard to the critical parameters under a wide variety of DVC scenarios. In case the unreliability of the derived local structure, a confidence parameter is introduced to prevent inappropriate penalizing. To find the reconstructed patch assignment with the largest expected probability in the context-adaptive MRF, the energy minimization for the MRF-based MAP estimate of the WZ frames is solved by global optimization and greedy strategies. Compared to the existing maximum probability and MMSE reconstruction with i.i.d. model, a better subjective and objective performance is validated by extensive experiments. [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]

Proposes a novel scene adaptive and signal adaptive quantization scheme capable of exploiting both the spectral and spatial localization properties resulting from wavelet transform. Subband scheme for image and video coding; Adaptive quantization of high-frequency subbands; Issues beyond quantization; Experimental results; Conclusions.

Presents a novel cellular connectionist model for the implementation of a clustering-based adaptive quantization in video coding applications. Adaptive quantization algorithm; Features of overall video coding scheme based on wavelet transform and adaptive quantization; Proof of network convergence.

Looks at a human face location technique based on contour extraction within the framework of a wavelet-based video compression scheme for videoconferencing applications. Use of semantic information of the human face to design a hybrid compression scheme for videoconferencing; Effectiveness of location technique in improving perceptual image quality.