We propose a new transform design method that targets the generation of compression-optimized transforms for next-generation multimedia applications. The fundamental idea behind transform compression is to exploit regularity within signals such that redundancy is minimized subject to a fidelity cost. Multimedia signals, in particular images and video, are well known to contain a diverse set of localized structures, leading to many different types of regularity and to nonstationary signal statistics. The proposed method designs sparse orthonormal transforms (SOT) that automatically exploit regularity over different signal structures and provides an adaptation method that determines the best representation over localized regions. Unlike earlier work that is motivated by linear approximation constructs and model-based designs that are limited to specific types of signal regularity, our work uses general nonlinear approximation ideas and a data-driven setup to significantly broaden its reach. We show that our SOT designs provide a safe and principled extension of the Karhunen-Loeve transform (KLT) by reducing to the KLT on Gaussian processes and by automatically exploiting non-Gaussian statistics to significantly improve over the KLT on more general processes. We provide an algebraic optimization framework that generates optimized designs for any desired transform structure (multi-resolution, block, lapped, etc.) with significantly better n-term approximation performance.For each structure, we propose a new prototype codec and test over a database of images. Simulation results show consistent increase in compression and approximation performance compared with conventional methods. Index Termssparse orthonormal transform, sparse lapped transform, sparse multi-resolution transform, transform optimization, image compression, nonlinear approximation 2
In this paper, we consider a hybrid P2P video on-demand architecture that utilizes both the server and the peer resources for efficient transmission of popular videos. In our system architecture, each peer dedicates some cache space to store a particular segment of a video file as well as some of its upload bandwidth to serve the cached segment to other peers. Peers join the system and issue a streaming request to a control server. Control server directs the peers to streaming servers or to other peers who have the desired video segments. Control server also decides which peer should cache which video segment. Our main contribution in this paper is to determine the proper caching strategies at peers such that we minimize the average load on the streaming servers.To minimize the server load, we pose the caching problem as a supply-demand-based utility optimization problem. By exploiting the inherent structure of a typical on-demand streaming application as well as the availability of a global view on the current supply-demand at the control server, we demonstrate how the system performance can be significantly improved over the brute-force caching decisions. In our analysis, we mainly consider three caching mechanisms. In the first mechanism (cache prefetching), a segment is prefetched to a given peer for caching purposes upon peer's arrival to the system regardless of whether that segment is currently demanded by that peer or not. In the second mechanism (opportunistic cache update), a peer has the option of replacing the segment that is currently in its cache with the last segment that it finished streaming. In the third mechanism, we combine both mechanisms as a hybrid caching strategy. In particular, we find that a dynamic-programming (DP)-based utility maximization solution using only the cache update method performs significantly better in reducing the server load. Furthermore, our findings suggest that even less sophisticated cache update solutions can perform almost as good as prefetching strategies in interesting regions of operation.Index Terms-Cache optimization, content distribution networks (CDN), peer-to-peer (P2P), streaming, supply-demand, video-on-demand (VoD).
This paper proposes a complete stochastic framework for RD optimal encoder design for video over error-prone networks, which applies to any motion-compensated predictive video codec. The distortion measure has been taken as the mean square error over an ensemble of channels given an estimate of the instantaneous packet loss probability. We show that 1) the optimal motion compensated prediction, in the MSE sense, requires computation of the expected value of the reference frames, and 2) calculation of the MSE (distortion measure) requires computation of the second moment of the reference frames. We propose a recursive procedure for the computation of both the expected value and second moment of the reference frames, which are together called the stochastic frame buffer. Furthermore, we propose a stochastic RD optimization method for selection of the optimal macroblock mode and motion vectors given the instantaneous packet loss probability. If available, channel feedback can also be incorporated into the proposed stochastic framework. However, the proposed framework does not require a feedback channel to exist, and when it exists, it does not have to be lossless. In the absence of any packet losses, the proposed stochastic framework reduces to the well-known deterministic RD optimization procedures. One possible application of the optimal stochastic framework would be for multicast streaming to an ensemble of receivers. Experimental results indicate that the proposed framework outperforms other available error tracking and control schemes.
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