We investigate multicast/broadcast of digital video over spread-spectrum CDMA cellular networks, a platform targeted at various kinds of multimedia services. In particular, we propose an end-to-end embedded transmission scheme which combines a scalable video source coder, adaptive power allocation, adaptive channel coding and an embedded multiresolution modulation strategy to simultaneously deliver a basic quality-of-service (QoS) to less capable receivers and an enhanced QoS for more capable receivers. We demonstrate the efficacy of this approach using the ITU-T H.263+ video source coder, although the approach is generally applicable to other scalable source coding schemes as well.
In a wireless ad hoc network, packets are sent from node-to-node in a multihop fashion until they reach the destination. In this paper we investigate the capacity of a wireless ad hoc network in supporting packet video transport. The ad hoc network consists of n homogeneous video users with each of them also serving as a relay node for other users. We investigate how the time delay aspects the video throughput in such an ad hoc network and how to provide a time-delay bounded packet video delivery service over such a network? The analytical results indicate that appropriate joint admission and power control have to be employed in order to efficiently utilize the network capacity while operating under the delay constraint as the distance between source and destination changes.
A Hyperspectral image is a sequence of images generated by collecting contiguously spaced spectral bands of data. One can view such an image sequence as a three-dimensional array of intensity values (pixels) within a rectangular prism. We present a Three-Dimensional Set Partitioned Embedded bloCK (3DSPECK) algorithm based on the observation that hyperspectral images are contiguous in the spectrum axis (this implies large inter-band correlations) and there is no motion between bands. Therefore, the three-dimensional discrete wavelet transform can fully exploit the inter-band correlations. A SPECK partitioning algorithm extended to three-dimensions is used to sort significant pixels. Rate distortion (Peak Signal-to-Noise Ratio (PSNR) vs. bit rate) performances were plotted by comparing 3DSPECK against 3DSPIHT on several sets of hyperspectral images. Results show that 3DSPECK is comparable to 3DSPIHT in hyperspectral image compression. 3DSPECK can achieve compression ratios in the approximate range of 16 to 27 while providing very high quality reconstructed images. It guarantees over 3 dB PSNR improvement at all rates or rate saving at least a factor of 2 over 2D coding of separate spectral bands without axial transformation.
The use of forward error-control (FEC) coding, possibly in conjunction with ARQ techniques, has emerged as a promising approach for video transport over ATM networks for cell-loss recovery and/or bit error correction, such as might be required for wireless links. Although FEC provides cell-loss recovery capabilities it also introduces transmission overhead which can possibly cause additional cell losses. A methodology is described to maximize the number of video sources multiplexed at a given quality of service (QoS), measured in terms of decoded cell loss probability, using interlaced FEC codes. The transport channel is modelled as a block interference channel (BIC) and the multiplexer as single server, deterministic service, finite buffer supporting N users. Based upon an information-theoretic characterization of the BIC and large deviation bounds on the buffer overflow probability, the described methodology provides theoretically achievable upper limits on the number of sources multiplexed. Performance of specific coding techniques using interlaced nonbinary Reed-Solomon (RS) codes and binary rate-compatible punctured convolutional (RCPC) codes is illustrated.
KEYWORDS: Digital filtering, Optical filters, Sensors, Signal to noise ratio, Image filtering, Target detection, Point spread functions, Image processing, Linear filtering, Statistical analysis
Detecting the presence of small weak targets in nonstationary clutter backgrounds is a fundamental problem in representative IR surveillance and tracking systems. In this paper, a system is proposed using spatiotemporal adaptive matched filtering to suppress the effects of clutter and enhance target detection. Shortfalls in conventional adaptive systems lead to a multiple parallel scanning approach to eliminate transients resulting from suboptimal filtering at clutter edges. Simulation results are provided which demonstrate that this approach provides substantially superior performance to a non-adaptive matched filter detection system design using global clutter statistics and, in some cases, can even achieve performance approximating that of an ideal fully-adaptive detector design from the complete statistical knowledge of the nonstationary clutter.
We describe a new approach to image sequence coding based upon variable-rate entropy- constrained subband coding (ECSBC) and, furthermore, develop the corresponding practical implementation of this ECSBC scheme for fixed-rate channels by extending previously developed adaptive entropy-coded quantization (AEC) techniques. In particular, a buffer- adaptive arithmetic-coded implementation of the ECSBC scheme, denoted ECSBC/AEC, is described which completely eliminates the generally associated encoder buffer overflow/underflow problems, even with a very small encoder buffer. This scheme utilizes hierarchial motion-compensated prediction in a backward-adaptive interframe coding system. Color image sequences are encoded in the YIQ domain. We demonstrate that this ECSBC/AEC scheme operating on real-world image sequences performs very close to the limiting performance achievable only with an encoder buffer of infinite size. Furthermore, we demonstrate that HDTV-quality image sequences can be encoded at bandwidths consistent with existing broadcast television systems. Finally, we show that this scheme delivers extremely robust performance under source mismatch conditions for both video-conferencing and HDTV video material.
Packetized video is likely to be one of the most significant users of bandwidth in future high- speed digital networks. In this paper we focus, in a unified manner, on the effects of packet losses, and the resulting error propagation, for a particular entropy-constrained subband coding (ECSBC) scheme employing hierarchical motion-compensated prediction (HMCP). We make use of the associated operational rate-distortion function to assess the quality of transmission that can be sustained under relatively low loss conditions representative of asynchronous transfer mode (ATM) networks. We show that the use of error-correcting codes helps in providing adequate protection for low-to-moderate packet loss. To capture the property of correlated loss in a network, a Markov chain model is used to represent packet losses.
KEYWORDS: Digital filtering, Signal to noise ratio, Linear filtering, Target detection, Receivers, Environmental sensing, Data modeling, Sensors, Optical filters, Model-based design
In the signal detection problem in nonstationary cluttered backgrounds using data from an imaging sensor, the receiver structure must generally be adapted to the local clutter statistics. Typically, the receive:r is implemented as a linear matched filter and the local adaptation consists of estimating and inverting a local covariance matrix to obtain the optimum weight vector. The local estimates of the inverse covariance matrix can be obtained in a variety of ways, but is generally a computationally expensive procedure and is prone to inaccuracies whenever estimation windows overlap clutter region boundaries. In the present paper we describe a simple but effective adaptive detection procedure which avoids some of the difficulties associated with existing schemes. This procedure employs a simple least mean-square (LMS) algorithm to adapt a linear matched filter to maximize local SNR. We describe a particular multiscan version of this algorithm with improved convergence properties. In particular, by implementing multiple parallel scanning patterns it's possible to avoid potential convergence problems at region boundaries associated with conventional single-scan adaptive approaches. Finally, we describe the performance of this scheme and compare its performance with competing approaches. We demonstrate performance approaching that achievable when perfect knowledge of local clutter statistics are available.
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