16772027
CCL-SVC: Optimizing user experience of broadcasting video on computation capability limited handheld devices
In this paper, we propose a novel scheme using computing complexity layered scalable video coding (CCLSVC) to optimize the user experience of broadcasting video in the computing capability limited handheld terminals. To address the heterogeneity of computing capability among different handheld devices, we employ hierarchal B reference structure of SVC to divide the frames into multiple computing complexity layers (CC Layers) in server side. The handheld clients simply choose to decode the frames in their corresponding layers in terms of their computation capability to maximize the video PSNR. We have proved that the optimal CC Layers division problem is a precedence constrained scheduling problem, which is an NP-complete problem. And we further propose our fast greedy method to approximately get optimized broadcasting video playback PSNR. The simulation shows that our method is superior to temporal SVC and random frame discarding method.
709845
Supporting Live Video on Ad Hoc Wireless Networks: Unbalanced Multiple Description Coding, Single Path Transport and Recursive Error Concealment
Robust transmission of live video over ad hoc wireless networks presents new challenges: high bandwidth requirements are coupled with delay constraints; nodes are constrained in processing power and storage capacity; ad hoc wireless networks suffer from bursty packet losses that drastically degrade the viewing experience. Accordingly, we propose a simple, but practical unbalanced multiple description (UMD) codec that uses single path transport only. At the UMD decoder, the lost Low- Resolution (LR) frames can be receded from the corresponding ones of the correctly received High- Resolution (HR) stream. This makes LR more robust to packet losses and ensures continuous video playback. The simulation results show that the proposed UMD codec has higher decoded quality, smaller quality fluctuation and lower probability of pause than other error resilience techniques, especially when channel burstiness becomes large. We also propose a novel sequence-based error concealment (EC) algorithm for our UMD decoder. It recursively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR description with respect to the LR description. In fact, the algorithm can be applied to most UMD schemes. It has the advantage of working on integer-, half- or quarter- pixel precision and looking backward without buffering future frames. Experimental results show that error decreases the fastest if the UMD decoder uses our EC algorithm. It can provide satisfactory performance on both objective and subjective evaluation.
38238585
Exploiting multi-scale support vector regression for image compression
Unlike traditional neural networks that require predefined topology of the network, support vector regression (SVR) approach can model the data within the given level of accuracy with only a small subset of the training data, which are called support vectors (SVs). This property of sparsity has been exploited as the basis for image compression. In this paper, for still image compression, we propose a multi-scale support vector regression (MS-SVR) approach, which can model the images with steep variations and smooth variations very well resulting in good performance. We test our proposed MS-SVR based algorithm on some standard images. The experimental results verify that the proposed MS-SVR achieves better performance than standard SVR. And in a wide range of compression ratio, MS-SVR is very close to JPEG in terms of peak signal-to-noise ratio (PSNR) but exhibits better subjective quality. Furthermore, MS-SVR even outperforms JPEG on both PSNR and subjective quality when the compression ratio is higher enough, for example 25:1 for Lena image. Even when compared with JPEG-2000, the results show greatly similar trend as those in JPEG experiments, except that the compression ratio is a bit higher where our proposed MS-SVR will outperform JPEG-2000.
1210599
Joint error concealment and error recovery for consecutive frame losses under the unbalanced multiple description coding architecture
Real-time video transmission over error-prone wireless networks often experiences consecutive frame losses due to either temporary link outages or traffic congestion. Although error concealment (EC) techniques have been extensively studied, they usually cannot handle the problem. Thus, we envision using EC under the unbalanced multiple description coding (UMDC) architecture. UMDC has almost no coding delay and can produce two descriptions at any bit-rates adaptive to different path bandwidths. In this paper, we propose an iterative EC algorithm able to adaptively exploit both the high-resolution (HR) and low-resolution (LR) information via multi-hypothesis weights. It is applied to both lost HR frames and following undecoded ones. Considering error propagation, we design an interframe error recovery (ER) algorithm for the undecoded HR frames. It iteratively uses multi-frame recovery principle to frame-by-frame reduce error drift in the HR stream with respect to the intermediate information from EC. The joint design of EC and ER can be applied to most UMD approaches. Extensive experiments have been carried out under different conditions. The proposed EC technique exhibits high PSNR gains versus the usual ones under the UMDC architecture and the classical ones without the support of UMDC. The proposed ER technique is efficient in reducing error drift. In conclusion, joint EC and ER can provide satisfactory performance on both PSNR and visual quality.
28617398
An Unbalanced Multiple Description Coding Scheme for Video Transmission Over Wireless Ad Hoc Networks
38198465
Control of a 3-dimensional snake-like robot
Biological snakes' diverse locomotion modes and physiology make them supremely adapted for environment. The special structure of snakes and their unique movement offer them peculiar ability of climbing and moving even in some ill-conditioned environments such as on the marshland or in narrow tubes. In this paper, we first discuss the controllability of a 3-dimensional snake-like robot, and then show the controllability of the robot by computer simulation.
16889097
Limited Feedback Precoding Based on Hierarchical Codebook and Linear Receiver
The analysis shows that the conventional codebook construction of Grassmannian subspace packing can not control the performance loss caused by a linear receiver, while a proper unitary perturbation to the codebook is capable of compensating for this performance loss. This paper therefore proposes a novel hierarchical codebook to exploit the gain of unitary perturbation. The hierarchical codebook consists of a Grassmannian subcodebook and a proposed perturbation subcodebook. To implement precoding, this paper also presents a successive codeword selection scheme, thus the receiver would successively selects two preferred codewords from Grassmannian and perturbation subcodebooks. With the feedback binary indices of these selections, the transmitter uses the product of two preferred codewords as the precoder. The theoretical analysis of the proposed precoding technique shows that the usage of the perturbation subcodebook can improve to a certain degree the system performance in terms of throughput as well as BER, with a small additional feedback overhead, and the proposed codebook would reduce the computational and storage requirements in contrast to the conventional codebook. It is also shown via computer simulations that the proposed technique gives a better BER performance than the single Grassmannian codebook based precoding technique does, even if the feedback overhead remains the same.
13102622
D-S Theory-based Trust Model FIRE^+ in Multi-agent Systems
In open multi-agent systems (MAS), interaction between agents is uncertain. Trust plays an important role in deciding with whom to interact and how to interact with it. Current trust models have solved the problem of how to quantify, deduce and evaluate trust, but there are still challenges. This paper presents FIRE+ trust model using utility loss of issue to evaluate trust values. With the representation of matrix, it efficiently increases the rating information. Also by introducing D-S theory and WMA, it can handle uncertain information and dishonest witness finely. Meanwhile, our approach introduces the concept of information-amount to derive each issue 's weight so as to gain the whole trust value of the target agent. It also takes witness reliability into account. Experiments show that FIRE+ is more effective than others.
6506776
A set theoretical method for video synopsis
A synopsis video presents a condensed video activities occurring during different periods, based on moving objects extracted in the spatial-temporal domain. How to place different object tubes with least collision in limited video length is crucial to synopsis performance but has not been thoroughly studied in previous work.
 In this paper, we address an important problem in video synopsis, that of object start-time programming. We formulate the problem in terms of set theory. An objective is derived to maximize visual information in video synopsis. After relaxing the problem to obtain a continuous one, the problem can be efficiently solved via mean-shift. The resulting algorithm can converge to the local optimum within a few iterations.
17395966
Scene segmentation based on video structure and spectral methods
Scene is an important semantic unit for video analysis, retrieval and browsing. However, due to the lack of a generic algorithm, many studies focus on specific methods for certain video genes, e.g., news, sports, etc. In this paper, we propose a general framework for scene segmentation. First, we construct a graph, in which the elements encode the shot-to-shot coherent characteristics of a video clip based on visual similarity and temporal relation between shots. In this step, we only exploit the inherent property of video itself and it is independent of video genres. Second, spectral method is applied on the graph to group shots into scenes. The proposed method is not only simple but also effective to deal with organized features. Experimental results validate the robustness of our method on different kinds of videos.