By Topic

Multimedia, IEEE Transactions on

Issue 4 • Date Aug. 2011

Filter Results

Displaying Results 1 - 25 of 29
  • Table of contents

    Page(s): C1 - C4
    Save to Project icon | Request Permissions | PDF file iconPDF (49 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Multimedia publication information

    Page(s): C2
    Save to Project icon | Request Permissions | PDF file iconPDF (129 KB)  
    Freely Available from IEEE
  • Content-Aware Display Adaptation and Interactive Editing for Stereoscopic Images

    Page(s): 589 - 601
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1981 KB) |  | HTML iconHTML  

    We propose a content-aware stereoscopic image display adaptation method which simultaneously resizes a binocular image to the target resolution and adapts its depth to the comfort zone of the display while preserving the perceived shapes of prominent objects. This method does not require depth information or dense correspondences. Given the specification of the target display and a sparse set of correspondences, our method efficiently deforms the input stereoscopic images for display adaptation by solving a least-squares energy minimization problem. This can be used to adjust stereoscopic images to fit displays with different real estates, aspect ratios and comfort zones. In addition, with slight modifications to the energy function, our method allows users to interactively adjust the sizes, locations and depths of the selected objects, giving users aesthetic control for depth perception. User studies show that the method is effective at editing depth and reducing occurrences of diplopia and distortions. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Video Inpainting on Digitized Vintage Films via Maintaining Spatiotemporal Continuity

    Page(s): 602 - 614
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (6251 KB) |  | HTML iconHTML  

    Video inpainting is an important video enhancement technique used to facilitate the repair or editing of digital videos. It has been employed worldwide to transform cultural artifacts such as vintage videos/films into digital formats. However, the quality of such videos is usually very poor and often contain unstable luminance and damaged content. In this paper, we propose a video inpainting algorithm for repairing damaged content in digitized vintage films, focusing on maintaining good spatiotemporal continuity. The proposed algorithm utilizes two key techniques. Motion completion recovers missing motion information in damaged areas to maintain good temporal continuity. Frame completion repairs damaged frames to produce a visually pleasing video with good spatial continuity and stabilized luminance. We demonstrate the efficacy of the algorithm on different types of video clips. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Practical Image Quality Metric Applied to Image Coding

    Page(s): 615 - 624
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1301 KB) |  | HTML iconHTML  

    Perceptual image coding requires an effective image quality metric, yet most of the existing metrics are complex and can hardly guide the compression effectively. This paper proposes a practical full-reference metric with consideration of the texture masking effect and contrast sensitivity function. The metric is capable of evaluating typical image impairments in real-world applications and can achieve the comparable performance as the state-of-the-art metrics on the publicly available subjectively-rated image databases. Due to its simplicity, the metric is embedded into JPEG image coding to ensure a better perceptual rate-distortion performance. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Adaptive Learning for Target Tracking and True Linking Discovering Across Multiple Non-Overlapping Cameras

    Page(s): 625 - 638
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1516 KB) |  | HTML iconHTML  

    To track targets across networked cameras with disjoint views, one of the major problems is to learn the spatio-temporal relationship and the appearance relationship, where the appearance relationship is usually modeled as a brightness transfer function. Traditional methods learning the relationships by using either hand-labeled correspondence or batch-learning procedure are applicable when the environment remains unchanged. However, in many situations such as lighting changes, the environment varies seriously and hence traditional methods fail to work. In this paper, we propose an unsupervised method which learns adaptively and can be applied to long-term monitoring. Furthermore, we propose a method that can avoid weak links and discover the true valid links among the entry/exit zones of cameras from the correspondence. Experimental results demonstrate that our method outperforms existing methods in learning both the spatio-temporal and the appearance relationship, and can achieve high tracking accuracy in both indoor and outdoor environment. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Bayesian Visual Reranking

    Page(s): 639 - 652
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1136 KB) |  | HTML iconHTML  

    Visual reranking has been proven effective to refine text-based video and image search results. It utilizes visual information to recover “true” ranking list from the noisy one generated by text-based search, by incorporating both textual and visual information. In this paper, we model the textual and visual information from the probabilistic perspective and formulate visual reranking as an optimization problem in the Bayesian framework, termed Bayesian visual reranking. In this method, the textual information is modeled as a likelihood, to reflect the disagreement between reranked results and text-based search results which is called ranking distance. The visual information is modeled as a conditional prior, to indicate the ranking score consistency among visually similar samples which is called visual consistency. Bayesian visual reranking derives the best reranking results by maximizing visual consistency while minimizing ranking distance. To model the ranking distance more precisely, we propose a novel pair-wise method which measure the ranking distance based on the disagreement in terms of pair-wise orders. For visual consistency, we study three different regularizers to mine the best way for its modeling. We conduct extensive experiments on both video and image search datasets. Experimental results demonstrate the effectiveness of our proposed Bayesian visual reranking. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Integrating Visual Saliency and Consistency for Re-Ranking Image Search Results

    Page(s): 653 - 661
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1356 KB) |  | HTML iconHTML  

    In this paper, we propose a new algorithm for image re-ranking in web image search applications. The proposed method focuses on investigating the following two mechanisms: 1) Visual consistency. In most web image search cases, the images that closely related to the search query are visually similar. These visually consistent images which occur most frequently in the first few web pages will be given higher ranks. 2) Visual saliency. From visual aspect, it is obvious that salient images would be easier to catch users' eyes, and it is observed that these visually salient images in the front pages are often relevant to the user's query. By integrating the above two mechanisms, our method can efficiently re-rank the images from search engines and obtain a more satisfactory search result. Experimental results on a real-world web image dataset demonstrate that our approach can effectively improve the performance of image retrieval. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Tag Tagging: Towards More Descriptive Keywords of Image Content

    Page(s): 662 - 673
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1932 KB) |  | HTML iconHTML  

    Tags have been demonstrated to be effective and efficient for organizing and searching social image content. However, these human-provided keywords are far from a comprehensive description of the image content, which limits their effectiveness in tag-based image search. In this paper, we propose an automatic scheme called tag tagging to supplement semantic image descriptions by associating a group of property tags with each existing tag. For example, an initial tag “tiger” may be further tagged with “white”, “stripes”, and “bottom-right” along three tag properties: color, texture, and location, respectively. In this way, the descriptive ability of the existing tags can be greatly enhanced. In the proposed scheme, a lazy learning approach is first applied to estimate the corresponding image regions of each initial tag, and then a set of property tags that correspond to six properties, including location, color, texture, size, shape, and dominance, are derived for each initial tag. These tag properties enable much more precise image search especially when certain tag properties are included in the query. The results of the empirical evaluation show that tag properties remarkably boost the performance of social image retrieval. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Analysis and Exploitation of Musician Social Networks for Recommendation and Discovery

    Page(s): 674 - 686
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1082 KB) |  | HTML iconHTML  

    This paper presents an extensive analysis of a sample of a social network of musicians. The network sample is first analyzed using standard complex network techniques to verify that it has similar properties to other web-derived complex networks. Content-based pairwise dissimilarity values between the musical data associated with the network sample are computed, and the relationship between those content-based distances and distances from network theory explored. Following this exploration, hybrid graphs and distance measures are constructed, and used to examine the community structure of the artist network. Finally, results of these investigations are shown to be mostly orthogonal between these distance spaces. These results are considered with a focus recommendation and discovery applications employing these hybrid measures as their basis. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Unifying Low-Level and High-Level Music Similarity Measures

    Page(s): 687 - 701
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2055 KB) |  | HTML iconHTML  

    Measuring music similarity is essential for multimedia retrieval. For music items, this task can be regarded as obtaining a suitable distance measurement between songs defined on a certain feature space. In this paper, we propose three of such distance measures based on the audio content: first, a low-level measure based on tempo-related description; second, a high-level semantic measure based on the inference of different musical dimensions by support vector machines. These dimensions include genre, culture, moods, instruments, rhythm, and tempo annotations. Third, a hybrid measure which combines the above-mentioned distance measures with two existing low-level measures: a Euclidean distance based on principal component analysis of timbral, temporal, and tonal descriptors, and a timbral distance based on single Gaussian Mel-frequency cepstral coefficient (MFCC) modeling. We evaluate our proposed measures against a number of baseline measures. We do this objectively based on a comprehensive set of music collections, and subjectively based on listeners' ratings. Results show that the proposed methods achieve accuracies comparable to the baseline approaches in the case of the tempo and classifier-based measures. The highest accuracies are obtained by the hybrid distance. Furthermore, the proposed classifier-based approach opens up the possibility to explore distance measures that are based on semantic notions. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Image Retagging Using Collaborative Tag Propagation

    Page(s): 702 - 712
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (959 KB) |  | HTML iconHTML  

    Photo sharing websites such as Flickr host a massive amount of social images with user-provided tags. However, these tags are often imprecise and incomplete, which essentially limits tag-based image indexing and related applications. To tackle this issue, we propose an image retagging scheme that aims at refining the quality of the tags. The retagging process is formulated as a multiple graph-based multi-label learning problem, which simultaneously explores the visual content of the images, semantic correlation of the tags as well as the prior information provided by users. Different from classical single graph-based multi-label learning algorithms, the proposed algorithm propagates the information of each tag along an individual tag-specific similarity graph, which reflects the particular relationship among the images with respect to the specific tag and at the same time the propagations of different tags interact with each other in a collaborative way with an extra tag similarity graph. In particular, we present a robust tag-specific visual sub-vocabulary learning algorithm for the construction of those tag-specific graphs. Experimental results on two benchmark Flickr image datasets demonstrate the effectiveness of our proposed image retagging scheme. We also show the remarkable performance improvements brought by retagging in the task of image ranking. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Probabilistic Novelty Detection for Acoustic Surveillance Under Real-World Conditions

    Page(s): 713 - 719
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (503 KB) |  | HTML iconHTML  

    Novelty detection in the machine learning context refers to identifying unknown/novel data, i.e., data which vary greatly from the ones that the system was trained with. This paper explores this technique as applied to acoustic surveillance of abnormal situations. The ultimate goal of the system is to help an authorized person towards taking the appropriate actions for preventing life/property loss. A wide variety of acoustic parameters is employed towards forming a multidomain feature vector, which captures diverse characteristics of the audio signals. Subsequently the feature coefficients are fed to three probabilistic novelty detection methodologies. Their performance is computed using two measures which take into account misdetections and false alarms. Out dataset was recorded under real-world conditions including three different locations where various types of normal and abnormal sound events were captured. A smart-home environment, an open public space, and an office corridor were used. The results indicate that probabilistic novelty detection can provide an accurate analysis of the audio scene to identify abnormal events. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Layered Internet Video Adaptation (LIVA): Network-Assisted Bandwidth Sharing and Transient Loss Protection for Video Streaming

    Page(s): 720 - 732
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1865 KB) |  | HTML iconHTML  

    As video traffic increases in the Internet and competes for limited bandwidth resources, it is important to design bandwidth-sharing and loss-protection schemes that account for video characteristics, beyond the traditional paradigm of fair-rate allocation among data flows. Ideally, such a scheme should handle both persistent and transient congestion as video streaming applications demand low-latency transmissions and low packet-loss ratios. This paper presents a novel scheme, layered Internet video adaptation (LIVA), in which network nodes feed back virtual congestion levels to video senders to assist both media-aware bandwidth sharing and transient-loss protection. The video senders respond to such feedback by adapting the rates of encoded scalable bitstreams based on their respective video rate-distortion (R-D) characteristics. The same feedback is employed to calculate the amount of forward error correction (FEC) protection for combating transient losses. Simulation studies show that LIVA can minimize the total distortion of all participating video streams and hence maximize their overall quality. At steady state, video streams experience no queueing delays or packet losses. In the face of transient congestion, the network-assisted adaptive FEC promptly protects video packets from losses. Our Linux-based demonstration showcases how LIVA can be implemented in a simple manner in real systems. We also present a solution for LIVA streams to coexist with TCP flows based on explicit congestion notification signaling. Finally, our theoretical analysis guarantees system stability for an arbitrary number of streams with round-trip delays below a prescribed limit. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • IRS: A Detour Routing System to Improve Quality of Online Games

    Page(s): 733 - 747
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1090 KB) |  | HTML iconHTML  

    Long network latency negatively impacts the performance of online games, and thus mechanisms are needed to mitigate its effects in order to provide a high-quality gaming experience. In this paper, we propose an indirect relay system (IRS) to forward game-state updates over detour paths in order to reduce the round-trip time (RTT) among players. We first collect extensive traces for RTTs among actual players in online games. We then analyze these traces to quantify the potential performance gain of the detour routing. Our analysis reveals that substantial reduction in the RTTs is possible. For example, our results indicate that more than 40% of players can observe at least 100 ms of RTT reduction by routing game-state updates through 1-hop detour paths. Because of the reduction in RTTs, players can join more gaming sessions that were not available to them due to long RTTs of the direct paths. Most importantly, we design and implement a complete IRS system for online games. To the best of our knowledge, this is the first system that directly reduces RTTs among players in online games, while previous works in the literature mitigate the long RTT issue by either hiding it from players or preventing players with high RTTs from being in the same game session. We implement the proposed IRS system and deploy it on 500 PlanetLab nodes. The results from real experiments show that the IRS system improves the online gaming quality from several aspects, while incurring negligible network and processing overheads. In particular, we observe that, with the proposed IRS system, more than 80% of game sessions achieve 100 ms or higher RTT reduction. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • The Impact of Spectrum Sensing Frequency and Packet-Loading Scheme on Multimedia Transmission Over Cognitive Radio Networks

    Page(s): 748 - 761
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (3029 KB) |  | HTML iconHTML  

    Recently, multimedia transmission over cognitive radio networks (CRNs) becomes an important topic due to the CR's capability of using unoccupied spectrum for data transmission. Conventional work has focused on typical quality-of-service (QoS) factors such as radio link reliability, maximum tolerable communication delay, and spectral efficiency. However, there is no work considering the impact of CR spectrum sensing frequency and packet-loading scheme on multimedia QoS. Here the spectrum sensing frequency means how frequently a CR user detects the free spectrum. Continuous, frequent spectrum sensing could increase the medium access control (MAC) layer processing overhead and delay, and cause some multimedia packets to miss the receiving deadline, and thus decrease the multimedia quality at the receiver side. In this research, we will derive the math model between the spectrum sensing frequency and the number of remaining packets that need to be sent, as well as the relationship between spectrum sensing frequency and the new channel availability time during which the CRN user is allowed to use a new channel (after the current channel is re-occupied by primary users) to continue packet transmission. A smaller number of remaining packets and a larger value of new channel availability time will help to transmit multimedia packets within a delay deadline. Based on the above relationship model, we select appropriate spectrum sensing frequency under single-channel case, and study the trade-offs among the number of selected channels, optimal spectrum sensing frequency, and packet-loading scheme under multi-channel case. The optimal spectrum sensing frequency and packet-loading solutions for multi-channel case are obtained by using the combination of Hughes-Hartogs and discrete particle swarm optimization (DPSO) algorithms. Our experiments of JPEG2000 packet-stream and H.264 video packet-stream transmission over CRN demonstrate the validity of our spectrum sensing frequency - - selection and packet-loading scheme. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Efficient Algorithms for Multi-Sender Data Transmission in Swarm-Based Peer-to-Peer Streaming Systems

    Page(s): 762 - 775
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (982 KB) |  | HTML iconHTML  

    In mesh-based peer-to-peer (P2P) streaming systems, each video sequence is divided into segments, which are then streamed from multiple senders to a receiver. The receiver needs to coordinate the senders by specifying a transmission schedule for each of them. We consider the problem of scheduling segment transmission in P2P streaming systems, where different segments have different weights in terms of quality improvements to the received video. Our goal is to compute the transmission schedule for each receiver in order to maximize the perceived video quality. We first show that this scheduling problem is NP-Complete. We then present an integer linear programming (ILP) formulation for it, so that it can be solved with any ILP solver. This optimal solution, however, is computationally expensive and is not suitable for real-time P2P streaming systems. Thus, we propose two approximation algorithms to solve this segment scheduling problem. These algorithms provide theoretical guarantees on the worst-case performance. The first algorithm considers the weight of each video segment. The second algorithm is simpler and it assumes that segments carry equal weights. We analyze the performance and complexity of the two algorithms. In addition, we rigorously evaluate the proposed algorithms with simulations and experiments using a prototype implementation. Our simulation and experimental results show that the proposed algorithms outperform other algorithms that are commonly used in deployed P2P streaming systems and that have been recently proposed in the literature. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Prioritized Distributed Video Delivery With Randomized Network Coding

    Page(s): 776 - 787
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1151 KB) |  | HTML iconHTML  

    We address the problem of prioritized video streaming over lossy overlay networks. We propose to exploit network path diversity via a novel randomized network coding (RNC) approach that provides unequal error protection (UEP) to the packets conveying the video content. We design a distributed receiver-driven streaming solution, where a client requests packets from the different priority classes from its neighbors in the overlay. Based on the received requests, a node in turn forwards combinations of the selected packets to the requesting peers. Choosing a network coding strategy at every node can be cast as an optimization problem that determines the rate allocation between the different packet classes such that the average distortion at the requesting peer is minimized. As the optimization problem has log-concavity properties, it can be solved with low complexity by an iterative algorithm. Our simulation results demonstrate that the proposed scheme respects the relative priorities of the different packet classes and achieves a graceful quality adaptation to network resource constraints. Therefore, our scheme substantially outperforms reference schemes such as baseline network coding techniques as well as solutions that employ rateless codes with built-in UEP properties. The performance evaluation provides additional evidence of the substantial robustness of the proposed scheme in a variety of transmission scenarios. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • A Robust Luby Transform Encoding Pattern-Aware Symbol Packetization Algorithm for Video Streaming Over Wireless Network

    Page(s): 788 - 796
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1470 KB) |  | HTML iconHTML  

    In this paper, we propose a Luby transform encoding pattern-aware symbol packetization algorithm to minimize the quality degradation of video streaming service caused by packet losses over wireless network. To achieve this goal, the relationship among Luby transform encoded symbols is analyzed based on Luby transform encoding pattern, and the proposed packetization algorithm is designed to minimize packet loss effects by reducing the dependency among packets conveying Luby transform encoded symbols. Finally, experimental results are provided to show the performance of the proposed algorithm. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • On-the-Fly Erasure Coding for Real-Time Video Applications

    Page(s): 797 - 812
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1310 KB) |  | HTML iconHTML  

    This paper introduces a robust point-to-point transmission scheme: Tetrys, that relies on a novel on-the-fly erasure coding concept which reduces the delay for recovering lost data at the receiver side. In current erasure coding schemes, the packets that are not rebuilt at the receiver side are either lost or delayed by at least one RTT before transmission to the application. The present contribution aims at demonstrating that Tetrys coding scheme can fill the gap between real-time applications requirements and full reliability. Indeed, we show that in several cases, Tetrys can recover lost packets below one RTT over lossy and best-effort networks. We also show that Tetrys allows to enable full reliability without delay compromise and as a result: significantly improves the performance of time constrained applications. For instance, our evaluations present that video-conferencing applications obtain a PSNR gain up to 7 dB compared to classic block-based erasure codes. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Cross-Layer Optimization for Downlink Wavelet Video Transmission

    Page(s): 813 - 823
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2251 KB) |  | HTML iconHTML  

    Cross-layer optimization for efficient multimedia communications is an important emerging issue towards providing better quality-of-service (QoS) over capacity-limited wireless channels. This paper presents a cross-layer optimization approach that operates between the application and physical layers to achieve high fidelity downlink video transmission by optimizing with respect to a quality criterion termed “visual entropy” using Lagrangian relaxation. By utilizing the natural layered structure of wavelet coding, an optimal level of power allocation is determined, which permits the throughput of visual entropy to be maximized over a multi-cell environment. A theoretical approach to optimization using the Shannon capacity and the Karush-Kuhn-Tucker (KKT) conditions is explored when coupling the application with the physical layers. Simulations show that the throughput gain for cross-layer optimization by visual entropy is increased by nearly 80% at the cell boundary as compared with peak signal-to-noise ratio (PSNR). View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Reduced-Reference Image Quality Assessment Using Reorganized DCT-Based Image Representation

    Page(s): 824 - 829
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (649 KB) |  | HTML iconHTML  

    In this paper, a novel reduced-reference (RR) image quality assessment (IQA) is proposed by statistical modeling of the discrete cosine transform (DCT) coefficient distributions. In order to reduce the RR data rates and further exploit the identical nature of the coefficient distributions between adjacent DCT subbands, the DCT coefficients are reorganized into a three-level coefficient tree. Subsequently, generalized Gaussian density (GGD) is employed to model the coefficient distribution of each reorganized DCT subband. The city-block distance is employed to measure the difference between the two images. Experimental results demonstrate that only a small number of RR features is sufficient for representing the image perceptual quality. The proposed method outperforms the RR WNISM and even the full-reference (FR) quality metric PSNR. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • Perceptually Guided Fast Compression of 3-D Motion Capture Data

    Page(s): 829 - 834
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (602 KB) |  | HTML iconHTML  

    A time efficient compression technique, incorporating attention stimulating factors, for motion capture data is proposed. Compression ratios of 25:1 to 30:1 can be achieved with very little noticeable degradation in perceptual quality of animation. Experimental analysis shows that the proposed algorithm is much faster than comparable approaches using wavelets, thereby making our approach feasible for motion capture, transmission, and real-time synthesis on mobile devices, where processing power and memory capacity are limited. View full abstract»

    Full text access may be available. Click article title to sign in or learn about subscription options.
  • IEEE Transactions on Multimedia EDICS

    Page(s): 835
    Save to Project icon | Request Permissions | PDF file iconPDF (16 KB)  
    Freely Available from IEEE
  • IEEE Transactions on Multimedia Information for authors

    Page(s): 836 - 837
    Save to Project icon | Request Permissions | PDF file iconPDF (127 KB)  
    Freely Available from IEEE

Aims & Scope

The scope of the Periodical is the various aspects of research in multimedia technology and applications of multimedia.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Chang Wen Chen
State University of New York at Buffalo