Hongxiang Li

Macroblock Classification Method for Video Applications Involving Motions

In this paper, a macroblock classification method is proposed for various video processing applications involving motions. Based on the analysis of the Motion Vector field in the compressed video, we propose to classify Macroblocks of each video frame into different classes and use this class information to describe the frame content. We demonstrate that this low-computation-complexity method can efficiently catch the characteristics of the frame. Based on the proposed macroblock classification, we further propose algorithms for different video processing applications, including shot change detection, motion discontinuity detection, and outlier rejection for global motion estimation. Experimental results demonstrate that the methods based on the proposed approach can work effectively on these applications.

A Fast Sub-Pixel Motion Estimation Algorithm for H.264/AVC Video Coding

Motion estimation (ME) is one of the most time-consuming parts in video coding. The use of multiple partition sizes in H.264/AVC makes it even more complicated when compared to ME in conventional video coding standards. It is important to develop fast and effective sub-pixel ME algorithms since: 1) the computation overhead by sub-pixel ME has become relatively significant while the complexity of integer-pixel search has been greatly reduced by fast algorithms, and 2) reducing sub-pixel search points can greatly save the computation for sub-pixel interpolation. In this letter, a novel fast sub-pixel ME algorithm is proposed which performs a “rough” sub-pixel search before the partition selection, and performs a “precise” sub-pixel search for the best partition. By reducing the searching load for the large number of non-best partitions, the computation complexity for sub-pixel search can be greatly decreased. Experimental results show that our method can reduce the sub-pixel search points by more than 50% compared to existing fast sub-pixel ME methods with negligible quality degradation.

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

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.

A new heat-map-based algorithm for human group activity recognition

In this paper, a new heat-map-based (HMB) algorithm is proposed for human group activity recognition. The proposed algorithm first models people trajectories as series of heat sources and then applies a thermal diffusion process to create a heat map (HM) for representing the group activities. Based on this heat map, a new surface-fitting (SF) method is also proposed for recognizing human group activities. Our proposed HM feature can efficiently keep the temporal motion information of the group activities while the proposed SF method can effectively catch the characteristics of the heat map for activity recognition. Experimental results demonstrate the effectiveness of our proposed algorithm.

An efficient framework for analyzing periodical activities in sports videos

In this paper, we propose a new and efficient framework for analyzing periodical activities in sports videos. The proposed framework first locates the motion patterns (MP) in each frame. By analyzing these located motion patterns with the proposed MP-analysis algorithm, the periodical sports activities can be effectively detected and counted. Furthermore, the qualified and unqualified actions can also be efficiently differentiated. Experimental results on various sports videos including dumbbell and weightlifting demonstrate the effectiveness of our proposed framework.

A new algorithm for improving Deblurring effects and addressing spatially-variant blur problems for image motion deblurring

Motion deblurring techniques have played important roles in many image processing applications. In this paper, a new algorithm is proposed for motion deblurring from a single image. The proposed algorithm introduces an anisotropic Patiral Differential Equation (PDE) method for latent image prediction and employs an adaptive optimization model in the blind deconvolution iterative step. Furthermore, in order to solve the spatially-variant blur problems, we also propose a Saliency-based Deblurring (SD) approach by introducing saliency extraction together with a compensate method for image deblurring. Experimental results have demonstrated the effectiveness of the proposed algorithm.

A new shot change detection method using information from motion estimation

In many video coding related applications, both shot change detection and motion estimation need to be performed. In this paper, a new shot change detection method is proposed for such applications, which uses the information from motion estimation. We first propose to classify Macroblocks of each frame into different classes based on the information obtained in the motion estimation process. Each class can capture some specific characteristics of the frame. The shot changes can then be detected based on the extracted class information. The proposed method has low computation complexity. It can easily be implemented into most existing video coding systems without extra cost. Experimental results show that the proposed method can detect shot changes effectively. Some extension applications of our class information are also discussed in the paper.

Achievable Degrees of Freedom of MIMO Y Channel With Delayed CSIT

In this letter, we study the degrees of freedom (DOF) for multiple-input-multiple-output (MIMO) Y channel in frequency division duplex (FDD) systems. Assuming each node has delayed channel state information at the transmitter (CSIT), we propose a physical layer network coding multi-stage transmission (PLNC-MST) scheme during broadcast (BC) phase. The results show that PLNC offers significant DOF gain in MIMO Y channel.

Retrospective Network Coding Alignment Over

Under the assumption that each node has delayed channel state information at transmitter (D-CSIT), this letter studies the achievable degrees of freedom (DOF) for K-user multiple-input-multiple-output (MIMO) Y channel in frequency division duplex (FDD) systems, where each user exchanges messages with other K-1 users simultaneously via an intermediate relay. We propose a new retrospective network coding alignment (RNCA) scheme and derive the closed-form expression for achievable DOF.

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It is well known that the Dirty Paper Coding (DPC) achieves the channel capacity for multiuser degraded channels. However, the optimality of DPC remains unknown for nondegraded channel. In this paper, we derive the optimal interference pre-cancellation order for a DPC based broadcast and unicast hybrid network. Specifically, we study different DPC cancellation schemes to maximize the hybrid capacity region. The conditions for each scheme being optimal are analytically derived. Our results show that the optimal interference precancellation order varies with SNR and broadcast and unicast channel conditions.