Efficient compression and network adaptive video coding for distributed video surveillance

The availability of cheap network based video cameras and the prevalence of wireless networks has lead to a major thrust towards deployment of large scale Distributed Video Surveillance (DVS) systems. This has opened up an important area of research to deal with the issues involved in DVS system for efficient collection and transmission of large scale video streams from the cameras at the guarded sites, to the end users in possibly constrained network conditions. In this paper, we propose a framework based on content-based video classification and scalable compression scheme to provide a robust bandwidth efficient video transmission for DVS. The scheme builds on a Discrete Wavelet Transform (DWT) based Color-Set Partitioning for Hierarchical Trees (CSPIHT) coding to obtain a scalable bitstream. Wavelet domain segmentation and compression assists in development of a DVS architecture. The architecture includes a novel module for dynamic allocation of Network bandwidth based on the current available resources and constraints. Different frame constituents are optimally coded based on their relative significance, perceptual quality, and available estimate of network bandwidth. Experimental result over different video sequences and simulations for Network conditions demonstrate the efficient performance of the approach.     

Performance of TZCD-MBOK watermarking scheme in T-DMB systems

Recently, watermarking based data transmission techniques using terrestrial digital TV signal have been proposed since they are also cost-free and can overcome the limitations of GPS. However, in the previous watermarking based methods, the detection accuracy is low and additional data rate is too low. Thus, we propose the throughput enhancement method by employing the TZCD-MBOK watermarking technique in T-DMB system. By applying the proposed scheme to T-DMB, it allows additional data transmission for disaster broadcasting and improves efficiency of data transmission in shadow region and indoor to mobile environment through watermarking spread code. From the simulation results, we confirm the proposed watermarking scheme affected on the existing T-DMB signal. Moreover, it was also confirmed that the system capacity increases as the power of additional watermarking signal rose. The results of the paper can be applied to wireless multimedia digital broadcasting systems.     

Intelligent copyright protection system using a matching video retrieval algorithm

Many video service sites headed by YouTube know what content requires copyright protection. However, they lack a copyright protection system that automatically distinguishes whether uploaded videos contain legal or illegal content. Existing protection techniques use content-based retrieval methods that compare the features of video. However, if the video encoding has changed in resolution, bit-rate or codec, these techniques do not perform well. Thus, this paper proposes a novel video matching algorithm even if the type of encoding has changed. We also suggest an intelligent copyright protection system using the proposed algorithm. This can serve to automatically prevent the uploading of illegal content. The proposed method has represented the accuracy of 97% with searching algorithm in video-matching experiments and 98.62% with automation algorithm in copyright-protection experiments. Therefore, this system could form a core technology that identifies illegal content and automatically excludes access to illegal content by many video service sites.     

A novel dynamic pricing scheme for contributing peers in the VoD system

In this paper we have proposed a dynamic pricing scheme for the contributing peers in the Video on Demand (VoD) system. The scheme provides an effective mechanism to maximize the profit through the residual resources of the contributing peers. A utilization function is executed for each contributing peer to estimate the utility factor based on the parameters such as initial setup cost, holding cost, chaining cost and salvage cost. In this paper, we urge an effective dynamic pricing algorithm that efficiently utilizes a range of parameters with a varying degree of complexity. The key findings of the algorithm are (i) each contributing peers are benefitted by the monetary based on its resource contributions to the VoD system and (ii) a high degree of social optimum is established by proficiently aggregating the contributing peer’s resources with the overall resources of the VoD system. We validate our claim by simulating the proposed dynamic pricing scheme with other standard pricing schemes such as altruism, cost model and game theory perspective. The result of our dynamic pricing scheme shows the best utility factor than other standard pricing schemes.     

Rate-smoothed schedule with tolerable data dropping for video coding stream

Most algorithms of smoothing schedule compute the required bit rate of video transmission to satisfy all the transmitted data. In this paper, our proposed tolerable data dropping algorithm can adjust transmitting data to fit available bit rate. MPEG-4 with fine grained scalability (FGS) can support partial data dropping to adapt to available bandwidth network. The algorithm is based on the minimum variance bandwidth allocation (MVBA) algorithm proposed by Salehi et al. to compute the bit rate such that still ensuring that the buffer never underflows and overflows for MPEG-4 FGS streams under the limited bandwidth resource. We prove that our proposed algorithm, named MVBADP, is smoother than the MVBA algorithm. The experimental results show the peak rate, the number of rate changes, and the ratio of total dropping data, and the PSNR for four test sequences with different content characteristics. They are varied by buffer sizes and tolerable dropping ratios. We found that the MVBADP algorithm can reduce the peak rate and the number of changes when the transmitted data are dropped by tolerable dropping ratio, especially on the video sequences with the high motion and complex texture characteristic and larger size change of the consecutive frame.     

Types and associated type families for hardware simulation and synthesis

In this article we overview the design and implementation of the second generation of Kansas Lava. Driven by the needs and experiences of implementing telemetry decoders and other circuits, we have made a number of improvements to both the external API and the internal representations used. We have retained our dual shallow/deep representation of signals in general, but now have a number of externally visible abstractions for combinatorial and sequential circuits, and enabled signals. We introduce these abstractions, as well as our abstractions for reading and writing memory. Internally, we found the need to represent unknown values inside our circuits, so we made aggressive use of associated type families to lift our values to allow unknowns, in a principled and regular way. We discuss this design decision, how it unfortunately complicates the internals of Kansas Lava, and how we mitigate this complexity. Finally, when connecting Kansas Lava to the real world, the standardized idiom of using named input and output ports is provided by Kansas Lava using a new monad, called Fabric. We present the design of this Fabric monad, and illustrate its use in a small but complete example.     

Extension of the DEMATEL method in an uncertain linguistic environment

The decision making trial and evaluation laboratory (DEMATEL) method is a useful tool for analyzing correlations among factors using crisp values. However, the crisp values are inadequate to model real-life situations due to the fuzziness and uncertainty that are frequently involved in judgments of experts. The aim of this paper is to extend the DEMATEL method to an uncertain linguistic environment. In this paper, the correlation information among factors provided by experts is in the form of uncertain linguistic terms. A formula is first presented to transform correlation information from uncertain linguistic terms to trapezoidal fuzzy numbers. Then, we aggregate the transformed correlation information of each expert into group information using the operations of trapezoidal fuzzy numbers. The importance and classification of factors are determined via fuzzy matrix operations. Furthermore, a causal diagram is constructed to vividly show the different roles of factors. Finally, an example is used to illustrate the procedure of the proposed method.