Shibao Zheng

Key distribution based on hierarchical access control for conditional access system in DTV broadcast

Conditional access system (CAS) is one of the key techniques in digital television (DTV) broadcast, which is used to charge the subscriber for subscribing fee by scrambling the program information. Scrambling algorithm and key distribution are the most important parts for CAS. In this paper, we proposed a new key distribution scheme based on hierarchical access control for conditional access system in digital television broadcast. Our key distribution scheme can greatly reduce the encrypting computation and acquire higher efficiency and security. Moreover, the proposed scheme is more flexible in processing joining and leaving of subscriber, which is very important for service provider to manage the subscriber.

A personalized TV guide system compliant with MHP

With the expansion of TV channels in digital era, viewers are exposed to information overload. Consequently, personalized guide systems are necessary to automatically customize program listings as well as to enable viewers to navigate and search programs. This paper proposes a personalized TV system running on standalone set-top box (STB) compliant with multimedia home platform (MHP) model. Besides the traditional functions, such as program navigation and search, this system is characterized by recommending program with high preferences on strategies of explicit and implicit feedback. Aiming at low end STB platform, we have deployed the personalized system in the MHP-compliant solution based on enhanced broadcasting profile. This paper also presents the implementing details and optimized strategies involved in information retrieving and program recommending. The experimental result shows that the proposed system presents a good performance.

H.264/Advanced Video Control Perceptual Optimization Coding Based on JND-Directed Coefficient Suppression

The field of video coding has been exploring the compact representation of video data, where perceptual redundancies in addition to signal redundancies are removed for higher compression. Many research efforts have been dedicated to modeling the human visual systems characteristics. The resulting models have been integrated into video coding frameworks in different ways. Among them, coding enhancements with the just noticeable distortion (JND) model have drawn much attention in recent years due to its significant gains. A common application of the JND model is the adjustment of quantization by a multiplying factor corresponding to the JND threshold. In this paper, we propose an alternative perceptual video coding method to improve upon the current H.264/advanced video control (AVC) framework based on an independent JND-directed suppression tool. This new tool is capable of finely tuning the quantization using a JND-normalized error model. To make full use of this new rate distortion adjustment component the Lagrange multiplier for rate distortion optimization is derived in terms of the equivalent distortion. Because the H.264/AVC integer discrete cosine transform (DCT) is different from classic DCT, on which state-of-the-art JND models are computed, we analytically derive a JND mapping formula between the integer DCT domain and the classic DCT domain which permits us to reuse the JND models in a more natural way. In addition, the JND threshold can be refined by adopting a saliency algorithm in the coding framework and we reduce the complexity of the JND computation by reusing the motion estimation of the encoder. Another benefit of the proposed scheme is that it remains fully compliant with the existing H.264/AVC standard. Subjective experimental results show that significant bit saving can be obtained using our method while maintaining a similar visual quality to the traditional H.264/AVC coded video.

The Large-Scale Crowd Behavior Perception Based on Spatio-Temporal Viscous Fluid Field

Over the past decades, a wide attention has been paid to crowd control and management in the intelligent video surveillance area. Among the tasks for automatic surveillance video analysis, crowd motion modeling lays a crucial foundation for numerous subsequent analysis but encounters many unsolved challenges due to occlusions among pedestrians, complicated motion patterns in crowded scenarios, etc. Addressing the unsolved challenges, the authors propose a novel spatio-temporal viscous fluid field to model crowd motion patterns by exploring both appearance of crowd behaviors and interaction among pedestrians. Large-scale crowd events are hereby recognized based on characteristics of the fluid field. First, a spatio-temporal variation matrix is proposed to measure the local fluctuation of video signals in both spatial and temporal domains. After that, eigenvalue analysis is applied on the matrix to extract the principal fluctuations resulting in an abstract fluid field. Interaction force is then explored based on shear force in viscous fluid, incorporating with the fluctuations to characterize motion properties of a crowd. The authors then construct a codebook by clustering neighboring pixels with similar spatio-temporal features, and consequently, crowd behaviors are recognized using the latent Dirichlet allocation model. The convincing results obtained from the experiments on published datasets demonstrate that the proposed method obtains high-quality results for large-scale crowd behavior perception in terms of both robustness and effectiveness.

The large-scale crowd density estimation based on sparse spatiotemporal local binary pattern

Over the past decade, a wide attention has been paid to the crowd control and management in intelligent video surveillance area. This paper proposes a sparse spatiotemporal local binary pattern (SST-LBP) descriptor to extract the dynamic texture of the walking crowd with the application to crowd density estimation. Firstly, the sparse selected location is extracted, which is notably variant in temporal domain and scale invariant in spatial domain. Afterwards, considering the spatial and temporal symmetry, the authors propose a sparse spatiotemporal local binary pattern algorithm and utilize its statistical property to describe the crowd feature. Finally, the crowd features are classified into a range of density levels by adopting support vector machine. The experiments on real video show that the proposed SST-LBP method is effective and robust on the large-scale crowd density estimation. Compared with the other methods, the proposed method does not base on the premise that the background should be extracted perfectly, which is too complicated to implement in real surveillance.

Crowd Event Perception Based on Spatio-temporal Viscous Fluid Field

Over the past decades, a wide attention has been paid to crowd control and management in intelligent video surveillance area. In this paper, the authors propose a novel spatiotemporal viscous fluid field to recognize large-scale crowd event with respect to both appearance and driven factor of crowd behavior. Firstly, a spatiotemporal variation matrix is proposed to exploit motion property of a crowd. In particular, the paper exploits characteristics of the matrix with eigenvalue decomposition algorithm and constructs an abstract fluid field to model the crowd motion pattern, which is denoted by spatiotemporal fluid field. Secondly, the paper proposes a spatiotemporal force field to exploit the interaction force between the pedestrians. Furthermore, the fluid and force field constructs a spatiotemporal viscous fluid field. Thirdly, after generating feature with bag of word model, the authors utilize latent Dirichlet allocation model to recognize crowd behavior. The experiments on PETS2009 and UMN datasets show that the proposed method has a better performance for large-scale crowd behavior perception in both robustness and effectiveness comparing with the conventional methods.

Raptor Codes Based Unequal Protection for Compressed Video According to Packet Priority

Raptor codes are state-of-the-art forward error correction (FEC) solutions for multimedia transmission, which have been applied to unequal error protection (UEP) of multi-layered media such as scalable video coding. In this paper, we address the problem of UEP for single-layered video over packet erasure channels. By exploiting the different priorities of video packets inside a group of pictures (GOP) and making full use of the good characteristics of standardized Raptor codes at large block length, we propose an optimized UEP framework for single-layered video and develop an efficient algorithm to solve it. Simulation results show that significant gains can be obtained by our method in case of packet losses.

Adaptive video-blocking artifact removal in discrete Hadamard transform domain

We present a novel postprocessing algorithm for blocking artifact removal in the discrete Hadamard transform (DHT) domain, which does not require prior knowledge of quantization parameters and features low computational complexity. All block-based video coding methods suffer annoying blocking artifacts at low bit rates. We first acquire edge information for the frame, then calculate block activities from DHT coefficients so as to classify smooth and coarse regions adaptively. Blocking artifacts are adaptively filtered in the DHT domain according to block activities. Experimental results show that the proposed method is able to remove blocking artifacts effectively while preserving image details and object edges well, and that it achieves better visual quality compared with other methods.

Design and implementation of transport stream demultiplexer in HDTV decoder SoC

A novel architecture of transport stream demultiplexer (TS Demux) in high definition television (HDTV) decoder is presented in this paper, which utilizes a cooperation scheme between hardware and software. TS parsing is decomposed into two phases: filtering and analyzing, which are fulfilled by hardware and software respectively. Meanwhile, the hardware filtering is controlled by the software. Two filtering layers, PID and section filtering, are supported by hardware, which can extract any format data in TS stream quickly. Compared to the current parsing scheme for TS packets, TS Demux discussed here needs less CPU resource and is easy to update as well.

A novel low-complexity and high-performance frame-skipping transcoder in DCT domain

This paper presents a novel frame-skipping transcoding scheme for improving picture quality and reducing computational complexity. Performing in the discrete cosine transform (DCT) domain, it eliminates the high computation from the process of DCT and inverse DCT (IDCT). To maintain an acceptable video quality after frame-rate conversion, the proposed scheme uses two strategies: one is transferring macroblock (MB) coding code by a judgment criterion to reduce the drift error, and the other is re-calculating the prediction residual to minimize the re-encoding error. Furthermore, the transcoder use a structure comprised of a separate decoder and a partial encoder to enhance the implementation efficiency. Simulation results show that, as compared to traditional transcoders, the proposed transcoder has a lower complexity and higher performance.