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This paper presents the generalized butterfly graph (GBG) and its application to video stream authentication. Compared with the original butterfly graph, the proposed GBG provides significantly increased flexibility, which is necessary for streaming applications, including supporting arbitrary bit-rate budget for authentication and arbitrary number of video packets. Within the GBG design, the problem of constructing an authentication graph is defined as follows: given the total number of packets to protect, the expected packet loss rate for the network, and the available overhead budget, how should one design the authentication graph to maximize the probability that the received packets are verifiable? Furthermore, given the fact that media packets are typically of unequal importance, we explore two variants of the GBG authentication, packet sorting and unequal authentication protection, which apply unequal treatment to different packets based on their importance. Lastly, we examine how the proposed GBG authentication can be applied within the context of rate-distortion-authentication (R-D-A) optimized streaming: given a media stream protected by GBG authentication, the R-D-A optimized streaming technique computes an optimized transmission schedule by recognizing and accounting for the authentication dependencies in the GBG authentication graph. Index Terms--Butterfly, generalized butterfly graph (GBG), R-D-A optimization, stream authentication.

Video communication is often afflicted by various forms of losses, such as packet loss over the Internet. This paper examines the question of whether the packet loss pattern, and in particular, the burst length, is important for accurately estimating the expected mean-squared error distortion resulting from packet loss of compressed video. We focus on the challenging case of low-bit-rate video where each P-frame typically fits within a single packet. Specifically, we: 1) verify that the loss pattern does have a significant effect on the resulting distortion; 2) explain why a loss pattern, for example a burst loss, generally produces a larger distortion than an equal number of isolated losses; and 3) propose a model that accurately estimates the expected distortion by explicitly accounting for the loss pattern, inter-frame error propagation, and the correlation between error frames. The accuracy of the proposed model is validated with H.264/AVC coded video and previous frame concealment, where for most sequences the total distortion is predicted to within [+ or -] 0.3 dB for burst loss of length two packets, as compared to prior models which underestimate the distortion by about 1.5 dB. Furthermore, as the burst length increases, our prediction is within [+ or -] 0.7 dB, while prior models degrade and underestimate the distortion by over 3 dB. The proposed model works well for video-telephony-type of sequences with low to medium motion. We also present a simple illustrative example, of how knowledge of the effect of burst loss can be used to adapt the schedule of video streaming to provide improved performance for a burst loss channel, without requiring an increase in bit rate. Index Terms--Distortion modeling, error propagation, error resilience, H.264/AVC, packet loss, rate-distortion optimization.

We define authenticated video as decoded video that results from those received packets whose authenticities have been verified. Generic data stream authentication methods usually impose overhead and dependency among packets for verification. Therefore, the conventional rate-distortion (R-D) optimized video streaming techniques produce highly sub-optimal R-D performance for authenticated video, since they do not account for the overhead and additional dependencies for authentication. In this paper, we study this practical problem and propose an Rate-Distortion-Authentication (R-D-A) optimized streaming technique for authenticated video. Based on packets' importance in terms of both video quality and authentication dependencies, the proposed technique computes a packet transmission schedule that minimizes the expected end-to-end distortion of the authenticated video at the receiver subject to a constraint on the average transmission rate. Simulation results based on H.264 JM 10.2 and NS-2 demonstrate that our proposed R-D-A optimized streaming technique substantially outperforms both prior (authentication-unaware) R-D optimized streaming techniques and data stream authentication techniques. In particular, when the channel capacity is below the source rate, the PSNR of authenticated video quickly drops to unacceptable levels using conventional R-D optimized streaming techniques, while the proposed R-D-A Optimization technique still maintains optimized video quality. Furthermore, we examine a low-complexity version of the proposed algorithm, and also an enhanced version which accounts for the multiple deadlines associated with each packet, which is introduced by stream authentication. Index Terms--Butterfly, digital signature, R-D optimization, R-D-A optimization, stream authentication.

We present a technique for low-complexity rate-distortion (R-D) optimized adaptive video streaming based on the concept of rate-distortion hint track (RDHT). RDHTs store the precomputed characteristics of a compressed media source that are crucial for high performance online streaming but difficult to compute in real time. This enables low-complexity adaptation to variations in transport conditions such as available data rate or packet loss. An RDHT-based streaming system has three components: 1) information that summarizes the R-D attributes of the media; 2) an algorithm for using the RDHT to predict the distortion for a feasible packet schedule; and 3) a method for determining the best packet schedule to adapt the streaming to the communication channel. A family of distortion models, denoted distortion chains, are presented which accurately predict the distortion produced by arbitrary packet loss patterns. Two distortion chain models are examined which lead to two RDHT-based techniques. We evaluate the proposed techniques for two canonical problems in streaming media, adaptation to available data rate and to packet loss. Experimental results demonstrate that for the difficult case of nonscalably coded H.264 video, the proposed systems provide significant performance gains over conventional low-complexity streaming systems, and achieve this gain with a comparable level of complexity making them suitable for online R-D optimized streaming. Index Terms--Distortion modeling, hint track (HT), low-complexity, multimedia streaming, rate-distortion (R-D), video adaptation, video coding.

A method providing improved optical flow estimation (OFE) accuracy for video at a conventional standard frame rate is proposed by initially capturing and processing the video at a higher frame rate. The method begins by estimating the optical flow between frames at high frame rate, and then accumulates and refines these estimates to produce accurate estimates of the optical flow at the desired standard frame rate.

A study on the use of post-processing in video compression proposes a novel, postprocessing algorithm to enhance the visual quality of low bit-rate, MC-block-discrete cosine transform-based image and video compression systems. The algorithm minimizes the unwanted blocking effects and mosquito noise artifacts while maintaining image sharpness and naturalness. It is relatively simple yet performs quite well in different situations.

Multiple description (MD) video coding can be used to reduce the detrimental effects caused by transmission over lossy packet networks. A number of approaches have been proposed for MD coding, where each provides a different tradeoff between compression efficiency and error resilience. How effectively each method achieves this tradeoff depends on the network conditions as well as on the characteristics of the video itself. This paper proposes an adaptive MD coding approach which adapts to these conditions through the use of adaptive MD mode selection. The encoder in this system is able to accurately estimate the expected end-to-end distortion, accounting for both compression and packet loss-induced distortions, as well as for the bursty nature of channel losses and the effective use of multiple transmission paths. With this model of the expected end-to-end distortion, the encoder selects between MD coding modes in a rate-distortion (R-D) optimized manner to most effectively tradeoff compression efficiency for error resilience. We show how this approach adapts to both the local characteristics of the video and network conditions and demonstrates the resulting gains in performance using an H.264-based adaptive MD video coder.