MPEG-coded Video Research Articles

Temporal Error Concealment for MPEG-Coded Video over Error-Prone Networks

Compressed video streams that transmitted over error prone networks may suffer severe degradation. Error concealment (EC) is one practical approach to combat with the error propagation. A number of EC techniques have been developed to diminish the influence of transmission errors. However, the techniques are always inefficient when the motions of object in a video are fast or complex. This paper proposes an adaptive temporal EC algorithm to precisely conceal the errors for MPEG-coded video. In the proposed method, each damaged macroblock is first divided into four equal sized sub-blocks. Both of the undamaged motion vectors and gray intensity information surrounding a damaged macroblock are performed to reconstruct the lost motion vectors of the sub-blocks based on boundary matching technique. Then the estimated motion vectors are utilized to reconstruct the damaged macroblock by exploiting the information from reference frames. Experimental results show that the visual quality and the PSNR evaluation of reconstructed frames are significantly improved by using the proposed EC algorithms. Thus the proposed method is expected to be a useful EC algorithm for motion vector compressed video over an error-prone network.

Evaluation of Neural Network Architectures for MPEG-4 Video Traffic Prediction

Multimedia applications and particularly MPEG-coded video streams are becoming major traffic components in high speed networks. Traffic prediction is important in enhancing the reliable operation over these networks. However, MPEG video traffic exhibits periodic correlation structure and a complex bit rate distribution, making prediction difficult. Neural networks can effectively be used to overcome such problem. In the literature, the problem has been mostly evaluated using standard feed-forward neural networks. However, a significant improvement can be expected using different types of neural networks. In this paper, six separate neural network predictors (including feed-forward) that can predict the basic frame types of MPEG-4: I, P, and B are developed and evaluated using long entertainment and broadcast video sequences. The performance is also compared to the widely used linear predictor. Comparison with results published in a recent work is also presented.

Short-term MPEG-4 video traffic prediction using ANFIS

Multimedia traffic and particularly MPEG-coded video streams are growing to be a major traffic component in high-speed networks. Accurate prediction of such traffic enhances the reliable operation and the quality of service of these networks through a more effective bandwidth allocation and better control strategies. However, MPEG video traffic is characterized by a periodic correlation structure, a highly complex bit rate distribution and very noisy streams. Therefore, it is considered an intractable problem. This paper presents a neuro-fuzzy short-term predictor for MPEG-4-coded videos. The predictor is based on the Adaptive Network Fuzzy Inference System (ANFIS) to perform single-step predictions for the I, P and B frames. Short-term predictions are also examined using smoothed signals of the video sequences. The ANFIS prediction results are evaluated using long entertainment and broadcast video sequences and compared to those obtained using a linear predictor. ANFIS is capable of providing accurate prediction and has the added advantage of being simple to design and to implement.

Sparse Basis Selection: New Results and Application to Adaptive Prediction of Video Source Traffic

Real-time prediction of video source traffic is an important step in many network management tasks such as dynamic bandwidth allocation and end-to-end quality-of-service (QoS) control strategies. In this paper, an adaptive prediction model for MPEG-coded traffic is developed. A novel technology is used, first developed in the signal processing community, called sparse basis selection. It is based on selecting a small subset of inputs (basis) from among a large dictionary of possible inputs. A new sparse basis selection algorithm is developed that is based on efficiently updating the input selection adaptively. When a new measurement is received, the proposed algorithm updates the selected inputs in a recursive manner. Thus, adaptability is not only in the weight adjustment, but also in the dynamic update of the inputs. The algorithm is applied to the problem of single-step-ahead prediction of MPEG-coded video source traffic, and the developed method achieves improved results, as compared to the published results in the literature. The present analysis indicates that the adaptive feature of the developed algorithm seems to add significant overall value.

Prediction of MPEG-coded video source traffic using recurrent neural networks

Predicting traffic generated by multimedia sources is needed for effective dynamic bandwidth allocation and for multimedia quality-of-service (QoS) control strategies implemented at the network edges. The time-series representing frame or visual object plane (VOP) sizes of an MPEG-coded stream is extremely noisy, and it has very long-range time dependencies. This paper provides an approach for developing MPEG-coded real-time video traffic predictors for use in single-step (SS) and multistep (MS) prediction horizons. The designed SS predictor consists of one recurrent network for I-VOPs and two feedforward networks for P- and B-VOPs, respectively. These are used for single-frame-ahead prediction. A moving average of the frame or VOP sizes time-series is generated from the individual frame sizes and used for both SS and MS prediction. The resulting MS predictor is based on recurrent networks, and it is used to perform two-step-ahead and four-step-ahead prediction, corresponding to multistep prediction horizons of 1 and 2 s, respectively. All of the predictors are designed using a segment of a single MPEG-4 video stream, and they are tested for accuracy on complete video streams with a variety of quantization levels, coded with both MPEG-1 and MPEG-4. Comparisons with SS prediction results of MPEG-1 coded video traces from the recent literature are presented. No similar results are available for prediction of MPEG-4 coded video traces and for MS prediction. These are considered unique contributions of this research.

Super-resolution still and video reconstruction from MPEG-coded video

There are a number of useful methods for creating high-quality video or still images from a lower quality video source. The best of these involve motion compensating a number of video frames to produce the desired video or still. These methods are formulated in the space domain and they require that the input be expressed in that format. More and more frequently, however, video sources are presented in a compressed format, such as MPEG, H.263, or DV. Ironically, there is important information in the compressed domain representation that is lost if the video is first decompressed and then used with a spatial-domain method. In particular, quantization information is lost once the video has been decompressed. Here, we propose a motion-compensated, transform-domain super-resolution procedure for creating high-quality video or still images that directly incorporates the transform-domain quantization information by working with the compressed bit stream. We apply this new formulation to MPEG-compressed video and demonstrate its effectiveness.

A real-time visual postprocessor for MPEG-coded video sequences

A novel content-adaptive enhancement filter is described, which aims at reducing compression artifacts in MPEG-coded video streams. The filter locally selects the most appropriate kernel among a set of pre-defined masks, based upon a classification of the pixels to be processed. The features used in the classification phase take into account the distribution of transform coefficients and the presence of nearby contour pixels, previously detected by an edge extractor. An important aspect of the proposed approach is the low computational complexity, very appealing in the scenario of a typical low-cost consumer application (video communication over the Internet, set-top-box DVB receiver, etc.). Experimental results show that the proposed algorithm outperforms existing approaches with similar level of complexity.

Real-time transport of MPEG video with a statistically guaranteed loss ratio in ATM networks

Unlike deterministic real-time communication in which excessive resources may be required for absolute performance guarantees, statistical real-time communication seeks to achieve both probabilistic performance guarantees and efficient resource sharing. This paper presents a framework for statistical real-time communication in ATM networks, providing delay-guaranteed transport of MPEG-coded video traffic with a statistically-guaranteed cell-loss ratio. Delay-guaranteed communication is achieved with a modified version of Traffic-Controlled Rate-Monotonic Priority Scheduling (TCRM). A set of statistical real-time channels that share similar traffic characteristics are multiplexed into a common macrochannel. Those statistical real-time channels which are multiplexed together share the resources of a macrochannel, and individual statistical real-time channels are given timeliness and probabilistic cell-loss guarantees. A macrochannel is serviced by the modified TCRM which improves link utilization and makes channel management simpler. Based on the analysis of an M/D/1/N queueing system, we propose a procedure for determining the transmission capacity of a macrochannel necessary to statistically guarantee a cell-loss ratio bound. Our extensive trace-driven simulation has shown the superiority of the proposed framework to the other approaches. The overall cell-loss ratios for multihop statistical real-time channels are shown to be smaller than the predetermined bounds, thus verifying our analytical results.

A new multi-level statistical model for variable bit rate MPEG sources over ATM networks and its performance study

In this paper, we propose a new traffic model for MPEG-coded video sequences. The proposed modeling scheme uses scene-based traffic characteristics and considers the correlations between frames of consecutive group of pictures (GOPs). Using a simple scene detection algorithm, scene changes are modeled by a state transition matrix and the number of GOPs of a scene state is modeled by a geometric distribution. Frames of a scene are modeled by the mean I, P, and B frame sizes of each state. For more accurate traffic modeling, the residual bits that represent the difference between the original frame size and the mean frame size of each frame type are compensated by autoregressive processes. The modeling results show that our scene-based model can capture the statistical traffic characteristics of the original video sequences well and estimate the queueing performance with good approximation quality.

An improved UDP protocol for video transmission over Internet-to-wireless networks

Packet video will become a significant portion of emerging and future wireless/Internet traffic. However, network congestion and wireless channel error yields tremendous packet loss and degraded video quality. In this paper, we propose a new complete user datagram protocol (CUDP), which utilizes channel error information obtained from the physical and link layers to assist error recovery at the packet level. We propose several maximal distance separable (MDS) code-based packet level error control coding schemes and derive analytical formulas to estimate the equivalent video frame loss for different versions of user datagram protocol (UDP). We validate the proposed packet coding and CUDP protocol using MPEG-coded video under various Internet packet loss and wireless channel profiles. Theoretic and simulation results show that the video quality can be substantially improved by utilizing the frame error information at UDP and application layer.

Efficient modeling of VBR MPEG-1 coded video sources

The performance evaluation of broadband networks requires statistical analysis and modeling of the actual network traffic. Since multimedia services, and especially variable bit rate (VBR) MPEG-coded video streams are expected to be a major traffic component carried by these networks, modeling of such services and accurate estimation of network resources are crucial for proper network design and congestion-control mechanisms that can guarantee the negotiated quality of service at a minimum cost. The layer modeling of MPEG-1 coded video streams and statistical analysis of their traffic characteristics at each layer is proposed, along with traffic models capable of estimating the network resources over asynchronous transfer mode (ATM) links. First, based on the properties of the entire MPEG-1 sequence (frame layer signal), a model (Model A) is presented by correlating three stochastic processes in discrete time (autoregressive models), each of which corresponds to the three types of frames of the MPEG encoder (I, P, and B frames). To simplify the traffic Model A and to reduce the required number of parameters, we study the MPEG stream at a higher layer by considering a signal, which expresses the average properties of I, P, and B frames over a group of picture (GOP) period. However, models on this layer cannot accurately estimate the network resources, especially in multiplexing schemes. For this reason, an intermediate layer is introduced, which exploits and efficiently combines information of both the aforementioned layers, producing a model (Model B), which requires much smaller number of parameters than Model A and simultaneously provides satisfactory results as far as the network resources are concerned. Evaluation of the validity of the proposed models is performed through experimental studies and computer simulations, using several long duration VBR MPEG-1 coded sequences, different from that used in modeling. The results indicate that both Models A and B are good estimators of video traffic behavior over ATM links at a wide range of utilization.

Efficient support for interactive scanning operations in MPEG-based video-on-demand systems

In this paper, we present an efficient approachfor supporting fast-scanning (FS) operations in MPEG-based video-on-demand (VOD) systems. This approach is based onstoring multiple, differently encoded versions of the samemovie at the server. A normal version is used for normalplayback, while several scan versions are used for FS. Eachscan version supports forward and backward FS at a givenspeedup. The server responds to an FS request by switching from the normal version to an appropriate scan version.Scanning versions are produced by encoding a sample of theraw frames using the same GOP pattern of the normal version. When a scanning version is decoded and played back atthe normal frame rate, it gives a perceptual motion speedup.By being able to control the traffic envelopes of the scanversions, our approach can be integrated into a previouslyproposed framework for distributing archived, MPEG-coded video streams. FS operations are supported using no or littleextra network bandwidth beyond what is already allocatedfor normal playback. Mechanisms for controlling the trafficenvelopes of the scan versions are presented. The actionstaken by the server and the client's decoder in response tovarious types of interactive requests are described in detail.The latency incurred in implementing various interactive requests is shown to be within an acceptable range. Stripingand disk-scheduling strategies for storing various versions atthe server are presented. Issues related to the implementationof our approach are discussed.

An objective measurement tool for MPEG video quality

A two-stage objective measurement model for MPEG-coded video is proposed. The first stage weights the coded video distortion according to the human visual system's response. It computes the frame-by-frame perceptual impairment in the decoded picture with respect to a reference picture; this includes low-pass spatial filtering, a Sobel operation to derive masking coefficients, and spatial masking on the raw error between reference and compressed pictures. The second stage, a cognitive emulator, provides a simulation of human high-level processing of visual information. This includes the very low temporal response of human viewers to image quality changes, and asymmetric behaviour in respect of picture quality changes from bad to good, and vice versa. With this model, we have been able to mimic quite accurately the temporally varying subjective picture quality of video sequences as recorded by the ITU-R SSCQE method.

An experimental digital consumer recorder for MPEG-coded video signals

The concept and real-time implementation of an experimental home-use digital recorder is presented, capable of recording MPEG-compressed video signals. The system has small recording mechanics based on the DVC standard and it uses MPEG compression for trick-mode signals as well.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>