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TOC Alert for Publication# 76 2014October 20<![CDATA[Table of contents]]>2410C1C4166<![CDATA[IEEE Transactions on Circuits and Systems for Video Technology publication information]]>2410C2C2140<![CDATA[Learning-Based Filter Selection Scheme for Depth Image Super Resolution]]>2410164116502459<![CDATA[Human Gait Recognition via Sparse Discriminant Projection Learning]]>(L_{1} ) and (L_{2} ) norms into the objective function of SBDA, two interrelated sparse discriminant subspaces can be obtained for gait feature extraction. Since the optimization problem has no closed-form solutions, an iterative method is designed to compute the optimal sparse subspace using the (L_{1}) and (L_{2} ) norms sparse regression. Theoretical analyses reveal the close relationship between SBDA and previous matrix-representation-based discriminant analysis methods. Since each nonzero element in each subspace is selected from the most important variables/factors, SBDA is potential to perform equivalent to or even better than the state-of-the-art subspace learning methods in gait recognition. Moreover, using the strategy of SBDA plus linear discriminant analysis (LDA), we can further improve the performance. A set of experiments on the standard USF HumanID and CASIA gait databases demonstrate that the proposed SBDA and SBDA + LDA can obtain competitive performance.]]>2410165116621550<![CDATA[Cross-View Action Recognition Using Contextual Maximum Margin Clustering]]>241016631668586<![CDATA[Dynamic Scene Understanding for Behavior Analysis Based on String Kernels]]>(k) , most of the similar trajectories to the one hand-drawn by an human operator. A set of normal trajectories’ models is extracted using a novel unsupervised learning technique: the scene is adaptively partitioned into zones using the distribution of the training set and each trajectory is represented as a sequence of symbols by considering positional information (the zones crossed in the scene), speed, and shape. The main novelty is the use of a kernel-based approach for evaluating the similarity between the trajectories. Furthermore, we define a novel and efficient kernel-based clustering algorithm, aimed at obtaining groups of normal trajectories. Experimentations, conducted over three standard data sets, confirm the effectiveness of the proposed approach.]]>2410166916815096<![CDATA[CAMHID: Camera Motion Histogram Descriptor and Its Application to Cinematographic Shot Classification]]>2410168216954352<![CDATA[View Synthesis Prediction in the 3-D Video Coding Extensions of AVC and HEVC]]>2410169617084556<![CDATA[Adaptive Inter-Mode Decision for HEVC Jointly Utilizing Inter-Level and Spatiotemporal Correlations]]>(times 2) N, inter 2N (times ) N, inter N (times 2) N, inter 2N (times ) nU, inter 2N (times ) nD, inter nL (times 2) N, inter nR (times 2) N, inter N (times ) N (only available for the smallest CU), intra 2N (times 2) N, and intra N (times ) N (only available for the smallest CU) in inter-frames. Similar to H.264/AVC, the mode decision process in HEVC is performed using all the possible depth levels (or CU sizes) and prediction modes to find the one with the least rate distortion (RD) cost using Lagrange multiplier. This achieves the highest coding efficiency, but leads to a very high computational complexity. Since the optimal prediction mode is highly content dependent, it is not efficient to use all the modes. In this paper, we propose a fast inter-mode decision algorithm for HEVC by jointly using the inter-level correlation of quadtree structure and the spatiotemporal correlation. There exist strong correlations of the prediction mode, the motion vector and RD cost between different depth levels and between spatially temporally a-
jacent CUs. We statistically analyze the prediction mode distribution at each depth level and the coding information correlation among the adjacent CUs. Based on the analysis results, three adaptive inter-mode decision strategies are proposed including early SKIP mode decision, prediction size correlation-based mode decision and RD cost correlation-based mode decision. Experimental results show that the proposed overall algorithm can save 49%–52% computational complexity on average with negligible loss of coding efficiency, exhibiting applicability to various types of video sequences.]]>2410170917226063<![CDATA[MC Complexity Reduction for Generalized P and B Pictures in HEVC]]>(L1) interpolation process when the (L0) and (L1) motion information of a bipredicted block are identical. The simulation results show that the time reductions of 14.5% and 6.4% for the encoder and decoder, respectively, were achieved for LD-B configuration without any changes in coding results. The proposed method was adopted in the HEVC test model as a non-normative complexity reduction tool.]]>2410172317282205<![CDATA[A Long-Term Reference Frame for Hierarchical B-Picture-Based Video Coding]]>(ldots ) ” and “IBBP(ldots ) ” through better exploitation of data correlation using reference frames and unequal quantization setting among frames. However, multiple reference frames (MRFs) techniques are not fully exploited in the HBP scheme because of the computational requirement for B-frames, unavailability of adjacent reference frames, and with no explicit sorting of the reference frames for foreground or background being used. To exploit MRFs fully and explicitly in background referencing, we observe that not a single frame of a video is appropriate to be the reference frame as no one covers adequate background of a video. To overcome the problems, we propose a new coding scheme with the HBP, which uses the most common frame in scene (McFIS), generated by background modeling, as a long-term reference (LTR) frame for the third unipredictive reference frame, so that foreground and background areas are expected to be referenced from the two frames in the HBP structure and the McFIS, respectively. There are two approaches to generate McFIS under the proposed methodology. In the first approach, we generate a McFIS using a number of original frames of a scene in a video and then encode it as an I-frame with a higher quality. For the rest of the scene, this generated I-frame is used as an LTR frame. In the second approach, we generate an McFIS from the decoded frames and then use it as an LTR frame, without the need to encode the McFIS. The first and the second approaches are suitable for a video with static background and dynamic background, respectively. In general, the second approach requires more computational time than that of t-
e the first approach. The experiments confirm that the proposed scheme outperforms three state-of-the-art algorithms by improving the image quality significantly with reduced computational time.]]>2410172917423064<![CDATA[Compressibility Constrained Sparse Representation With Learnt Dictionary for Low Bit-Rate Image Compression]]>(ell _{1}) -norm optimization problem of sparse representation. Given these coefficients, each image patch can be represented by the linear combination of texture elements encoded in an over-complete dictionary, learnt from other training images. Finally, low bit-rate image compression can be achieved, owing to the sparsity and compressibility of coefficients by our CCSR approach. The experimental results demonstrate the effectiveness and superiority of the CCSR approach on compressing the natural and remote sensing images at low bit-rates.]]>2410174317577461<![CDATA[Relay-Assisted Multiuser Video Streaming in Cognitive Radio Networks]]>2410175817701908<![CDATA[Epipolar Geometry-Based Side Information Creation for Multiview Wyner–Ziv Video Coding]]>2410177117864851<![CDATA[A Real-Time Motion-Feature-Extraction VLSI Employing Digital-Pixel-Sensor-Based Parallel Architecture]]>2410178717994309<![CDATA[A High-Efficiency and High-Accuracy Fully Automatic Collaborative Face Annotation System for Distributed Online Social Networks]]>(F) -measure and Similarity accuracy rates that were, respectively, 64.03% and 63.05% higher for the proposed method in comparison to other state-of-the-art face annotation methods, as well as demonstrating that our method can result in a reduction in overall processing time of 78.06%.]]>2410180018133933<![CDATA[Visibility Restoration of Single Hazy Images Captured in Real-World Weather Conditions]]>2410181418246317<![CDATA[Offset Compensation Method for Skip Mode in Hybrid Video Coding]]>2410182518311308<![CDATA[Open Access]]>2410183218321156<![CDATA[IEEE Circuits and Systems Society Information]]>2410C3C3119