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Circuits and Systems for Video Technology, IEEE Transactions on

Issue 5 • Date May 2014

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Displaying Results 1 - 21 of 21
  • Table of contents

    Page(s): C1 - C4
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  • IEEE Transactions on Circuits and Systems for Video Technology publication information

    Page(s): C2
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  • Blind Post-Processing for Ringing and Mosquito Artifact Reduction in Coded Videos

    Page(s): 721 - 732
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    Block-based video-coding standards produce unwanted spatial and temporal artifacts in reconstructed videos. Among them, ringing and mosquito artifacts arise due to the quantization of high-frequency discrete cosine transform coefficients. Most of the existing artifact-reduction algorithms assume that coding information such as a standard quantization table and the corresponding quantization parameter for each block are available. In many multimedia applications, however, external video inputs are usually supplied without coding information. To effectively reduce the ringing and mosquito artifacts in a decoded input video sequence, it is necessary to control the filter strength block by block. In this paper, we present a blind block-based method to estimate the quantization amount and propose a novel post-processing algorithm based on the visibility of artifacts in terms of the human visual system using the estimated quantization amount. Experimental results demonstrate that the proposed algorithm better alleviates ringing and mosquito artifacts in various coded videos compared with the existing algorithms. View full abstract»

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  • Random-Grid-Based Visual Cryptography Schemes

    Page(s): 733 - 744
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    This paper discusses a random-grid-based nonexpanded visual cryptography scheme for generating both meaningful and noise-like shares. First, the distribution of black pixels on the share images and the stack image is analyzed. A probability allocation method is then proposed that is capable of producing the best contrast in both the share images and the stack image. With our method, not only can different cover images be used to hide the secret image, but the contrast can be adjusted as needed. The most important result is the improvement of the visual quality of both the share images and the stack image to their theoretical maximum. Our meaningful visual secret sharing method is shown in experiments to be superior to past methods. View full abstract»

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  • Visual Comfort Amelioration Technique for Stereoscopic Images: Disparity Remapping to Mitigate Global and Local Discomfort Causes

    Page(s): 745 - 758
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    This paper proposes a new disparity remapping framework to improve the visual comfort of stereoscopic images. The proposed framework adaptively remaps disparities of a scene according to different causes of visual discomfort. A linear disparity remapping is first performed in order to address visual discomfort induced by excessive disparities. This linear remapping changes the disparities of the scene to obtain an overall target disparity range. Then, a nonlinear disparity remapping process selectively adjusts the disparity of problematic local disparity ranges according to their contribution to the visual discomfort. The proposed nonlinear disparity remapping process enables us to minimize the loss in perceived depth range while further improving visual comfort. The effectiveness of the proposed disparity remapping framework has been successfully evaluated by subjective assessments of visual comfort and naturalness. Experimental results demonstrate the validity of the proposed remapping framework. More importantly, we show that the nonlinear refinement of disparity in problematic regions can efficiently improve visual comfort while maintaining the naturalness of the scene. View full abstract»

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  • Object-Coherence Warping for Stereoscopic Image Retargeting

    Page(s): 759 - 768
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    This paper addresses the topic of content-aware stereoscopic image retargeting. The key to this topic is consistently adapting a stereoscopic image to fit displays with various aspect ratios and sizes while preserving visually salient content. Most methods focus on preserving the disparities and shapes of visually salient objects through nonlinear image warping, in which distortions caused by warping are propagated to homogenous and low-significance regions. However, disregarding the consistency of object deformation sometimes results in apparent distortions in both the disparities and shapes of objects. An object-coherence warping scheme is proposed to reduce this unwanted distortion. The basic idea is to utilize the information of matched objects rather than that of matched pixels in warping. Such information implies object correspondences in a stereoscopic image pair, which allows the generation of an object significance map and the consistent preservation of objects. This strategy enables our method to consistently preserve both the disparities and shapes of visually salient objects, leading to good content-aware retargeting. In the experiments, qualitative and quantitative analyses of various stereoscopic images show that our results are better than those generated by related methods in terms of consistency of object preservation. View full abstract»

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  • Region-Based Saliency Detection and Its Application in Object Recognition

    Page(s): 769 - 779
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    The objective of this paper is twofold. First, we introduce an effective region-based solution for saliency detection. Then, we apply the achieved saliency map to better encode the image features for solving object recognition task. To find the perceptually and semantically meaningful salient regions, we extract superpixels based on an adaptive mean shift algorithm as the basic elements for saliency detection. The saliency of each superpixel is measured by using its spatial compactness, which is calculated according to the results of Gaussian mixture model (GMM) clustering. To propagate saliency between similar clusters, we adopt a modified PageRank algorithm to refine the saliency map. Our method not only improves saliency detection through large salient region detection and noise tolerance in messy background, but also generates saliency maps with a well-defined object shape. Experimental results demonstrate the effectiveness of our method. Since the objects usually correspond to salient regions, and these regions usually play more important roles for object recognition than background, we apply our achieved saliency map for object recognition by incorporating a saliency map into sparse coding-based spatial pyramid matching (ScSPM) image representation. To learn a more discriminative codebook and better encode the features corresponding to the patches of the objects, we propose a weighted sparse coding for feature coding. Moreover, we also propose a saliency weighted max pooling to further emphasize the importance of those salient regions in feature pooling module. Experimental results on several datasets illustrate that our weighted ScSPM framework greatly outperforms ScSPM framework, and achieves excellent performance for object recognition. View full abstract»

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  • Unsupervised Object Extraction by Contour Delineation and Texture Discrimination Based on Oriented Edge Features

    Page(s): 780 - 788
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    This paper presents an unsupervised object extraction system that extracts a single object from natural scenes without relying on color information. The contour information and texture information are analyzed through separate oriented-edge-based processing channels and then combined to complement each other. Contour candidates are extracted from multiresolution edge maps, whereas the local texture information is compactly represented by an oriented-edge-based feature vector and then analyzed by K-means clustering. The object region is determined by merging the results of two separate analysis channels based on the simple assumption that the object is located centrally in the scene. As a result, the object region has been successfully extracted from the scene with a well-defined single boundary line. Both subjective and objective evaluations were carried out and it is shown that the proposed algorithm handles the challenges of complex background well, using only gray-scale images. View full abstract»

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  • Unsupervised Multiclass Region Cosegmentation via Ensemble Clustering and Energy Minimization

    Page(s): 789 - 801
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    The problem of unsupervised segmentation of multi-class regions can be significantly boosted when they irregularly recur in multiple images. The existing segmentation methods are either weakly supervised, such as tagging images with object classes, or are limited by the assumption that each image contains all the object instances. In this paper, we propose a new method to cosegment multiclass regions from a group of images without the assumption about object configurations. The key idea is to discover the unknown object-like proposals via a robust ensemble clustering scheme. The proposals are then used to derive unary and pairwise energy potentials across all the images, which can be minimized with the α-expansion. Experimental evaluation on a number of image groups demonstrates the good performance of the proposed method on the multiclass region cosegmentation. View full abstract»

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  • Face Hallucination Via Weighted Adaptive Sparse Regularization

    Page(s): 802 - 813
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    Sparse representation-based face hallucination approaches proposed so far use fixed ℓ1 norm penalty to capture the sparse nature of face images, and thus hardly adapt readily to the statistical variability of underlying images. Additionally, they ignore the influence of spatial distances between the test image and training basis images on optimal reconstruction coefficients. Consequently, they cannot offer a satisfactory performance in practical face hallucination applications. In this paper, we propose a weighted adaptive sparse regularization (WASR) method to promote accuracy, stability and robustness for face hallucination reconstruction, in which a distance-inducing weighted ℓq norm penalty is imposed on the solution. With the adjustment to shrinkage parameter q , the weighted ℓq penalty function enables elastic description ability in the sparse domain, leading to more conservative sparsity in an ascending order of q . In particular, WASR with an optimal q > 1 can reasonably represent the less sparse nature of noisy images and thus remarkably boosts noise robust performance in face hallucination. Various experimental results on standard face database as well as real-world images show that our proposed method outperforms state-of-the-art methods in terms of both objective metrics and visual quality. View full abstract»

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  • Face Authentication With Makeup Changes

    Page(s): 814 - 825
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    Recent studies have shown that facial cosmetics have an impact on face recognition. To develop a face recognition system that is robust to facial makeup, we propose performing correlation mapping between makeup and nonmakeup faces on features extracted from local patches. Three methods are explored to learn the correlations. We also study the problem of makeup detection. Four categories of features are proposed to characterize cosmetics, including skin color tone, skin smoothness, texture, and highlight. A patch selection scheme and discriminative mapping are presented to enhance the performance of makeup detection. A complete system is then developed for face verification utilizing the makeup detection result. Experimental results show that our system is robust to cosmetics in face authentication. An accuracy of about 80.0% can be achieved on a database of about 500 pairs of makeup and nonmakeup face images. View full abstract»

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  • A New Network-Based Algorithm for Human Activity Recognition in Videos

    Page(s): 826 - 841
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    In this paper, a new network-transmission-based (NTB) algorithm is proposed for human activity recognition in videos. The proposed NTB algorithm models the entire scene as an error-free network. In this network, each node corresponds to a patch of the scene and each edge represents the activity correlation between the corresponding patches. Based on this network, we further model people in the scene as packages, while human activities can be modeled as the process of package transmission in the network. By analyzing these specific package transmission processes, various activities can be effectively detected. The implementation of our NTB algorithm into abnormal activity detection and group activity recognition are described in detail in this paper. Experimental results demonstrate the effectiveness of our proposed algorithm. View full abstract»

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  • A Fast Simple Optical Flow Computation Approach Based on the 3-D Gradient

    Page(s): 842 - 853
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    Optical flow estimation is a fundamental task of many computer vision applications. In this paper, we propose a fast simple algorithm to compute optical flow based on the 3-D gradient in video sequences. Although the algorithm does not provide highly accurate results, it is computationally simple and fast, and the output is applicable for many applications. The basic idea is that points will form trajectories in video sequences, and the trajectory between two frames of each point is approximated as a straight line, which is the tangent of the trajectory in our algorithm. Therefore, the optical flow of each point is the projecting line of the straight line, which represents its trajectory, in the image plane. Experimental results show that the proposed algorithm is efficient and effective, and is of satisfying accuracy on angle. It is able to provide effective optical flow results for real-time applications. View full abstract»

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  • Part-Based Online Tracking With Geometry Constraint and Attention Selection

    Page(s): 854 - 864
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    Visual tracking in condition of occlusion, appearance or illumination change has been a challenging task over decades. Recently, some online trackers, based on the detection by classification framework, have achieved good performance. However, problems are still embodied in at least one of the three aspects: 1) tracking the target with a single region has poor adaptability for occlusion, appearance or illumination change; 2) lack of sample weight estimation, which may cause overfitting issue; and 3) inadequate motion model to prevent target from drifting. For tackling the above problems, this paper presents the contributions as follows: 1) a novel part-based structure is utilized in the online AdaBoost tracking; 2) attentional sample weighting and selection is tackled by introducing a weight relaxation factor, instead of treating the samples equally as traditional trackers do; and 3) a two-stage motion model, multiple parts constraint, is proposed and incorporated into the part-based structure to ensure a stable tracking. The effectiveness and efficiency of the proposed tracker is validated upon several complex video sequences, compared with seven popular online trackers. The experimental results show that the proposed tracker can achieve increased accuracy with comparable computational cost. View full abstract»

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  • Metric Learning Based Structural Appearance Model for Robust Visual Tracking

    Page(s): 865 - 877
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    Appearance modeling is a key issue for the success of a visual tracker. Sparse representation based appearance modeling has received an increasing amount of interest in recent years. However, most of existing work utilizes reconstruction errors to compute the observation likelihood under the generative framework, which may give poor performance, especially for significant appearance variations. In this paper, we advocate an approach to visual tracking that seeks an appropriate metric in the feature space of sparse codes and propose a metric learning based structural appearance model for more accurate matching of different appearances. This structural representation is acquired by performing multiscale max pooling on the weighted local sparse codes of image patches. An online multiple instance metric learning algorithm is proposed that learns a discriminative and adaptive metric, thereby better distinguishing the visual object of interest from the background. The multiple instance setting is able to alleviate the drift problem potentially caused by misaligned training examples. Tracking is then carried out within a Bayesian inference framework, in which the learned metric and the structure object representation are used to construct the observation model. Comprehensive experiments on challenging image sequences demonstrate qualitatively and quantitatively that the proposed algorithm outperforms the state-of-the-art methods. View full abstract»

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  • Robust Video Coding Based on Hybrid Hierarchical B Pictures

    Page(s): 878 - 888
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    Compressed video streams transmitted over error-prone environments are usually corrupted by transmission errors. Error-concealment techniques can be used to recover the lost information. In this paper, a hybrid model is proposed to improve the error-concealment performance. The model combines two hierarchical B-picture coding structures such that key-frames, reference B frames, or even nonreference B frames have buddy frames to serve as their data recovery frames when they are lost. With buddy frames, the distance between a lost frame and its recovering frame can be substantially reduced. In addition, an improved estimation method is also proposed to further increase the accuracy of recovering motion. Error-concealment performance can thus be significantly improved with little bit-rate redundancy. We have conducted experiments to compare its performance with other methods, and the results show that the proposed hybrid model outperforms these competed methods. The advantages of the proposed hybrid model are demonstrated in error-free and packet-loss environments. View full abstract»

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  • Distributed Wireless Video Scheduling With Delayed Control Information

    Page(s): 889 - 901
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    Traditional distributed wireless video scheduling is based on perfect control channels in which instantaneous control information from the neighbors is available. However, it is difficult, sometimes even impossible, to obtain this information in practice, especially for dynamic wireless networks. Thus, neither the distortion-minimum scheduling approaches aiming to meet the longterm video quality demands nor the solutions that focus on minimum delay can be applied directly. This motivates us to investigate the distributed wireless video scheduling with delayed control information (DCI). First, to exploit in a tractable framework, we translate this scheduling problem into a stochastic optimization rather than a convex optimization problem. Next, we consider two classes of DCI distributions: 1) the class with finite mean and variance and 2) a general class that does not employ any parametric representation. In each case, we study the relationship between the DCI and scheduling performance, and provide a general performance property bound for any distributed scheduling. Subsequently, a class of distributed scheduling scheme is proposed to achieve the performance bound by making use of the correlation among the time-scale control information. Finally, we provide simulation results to demonstrate the correctness of the theoretical analysis and the efficiency of the proposed scheme. View full abstract»

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  • Special issue on visual computing in the cloud: Cloud gaming and virtualization

    Page(s): 902
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  • Special issue on visual computing in the cloud: Mobile computing

    Page(s): 903
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  • IEEE Transactions on Circuits and Systems for Video Technology information for authors

    Page(s): 904
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  • IEEE Circuits and Systems Society Information

    Page(s): C3
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Aims & Scope

The emphasis is focused on, but not limited to:
1. Video A/D and D/ A
2. Video Compression Techniques and Signal Processing
3. Multi-Dimensional Filters and Transforms
4. High Speed Real-Tune Circuits
5. Multi-Processors Systems—Hardware and Software
6. VLSI Architecture and Implementation for Video Technology 

 

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Dan Schonfeld
Multimedia Communications Laboratory
ECE Dept. (M/C 154)
University of Illinois at Chicago (UIC)
Chicago, IL 60607-7053
tcsvt-eic@tcad.polito.it

Managing Editor
Jaqueline Zelkowitz
tcsvt@tcad.polito.it