52859343
Towards rate-distortion tradeoff in real-time color video coding
In this paper, we address to the key problem in real-time video coding, the rate-distortion tradeoff. We found that the distribution of the integer transform coefficients in H.264 color video coding can be approximated by Laplacian distribution but the rate-distortion characteristics should be modelled for the luminance and chrominance components separately. So we propose separable rate-distortion models for color video coding. The feedback from the encoder buffer is analyzed by a control-theoretic adaptation approach. To achieve smooth video quality and satisfy the delay constraints in real-time applications, a novel rate distortion tradeoff controller is employed. Simulations results demonstrate that the proposed approach can achieve smooth video quality without sacrificing overall quality while efficiently utilize the bandwidth without buffer overflow or underflow.
40433168
Linear rate-distortion models for MPEG-4 shape coding
In this letter, we explore the rate-distortion (R-D) characteristics of binary shape information in the MPEG-4 standard. The shape coding scheme is a block-based context-based arithmetic encoding approach and distortion is introduced by down and up sampling. Generally, the shape bit rate and nonnormalized distortion increase in proportion with the number of border blocks. At any given resolution scale, the more complex an object, the more distortion that is introduced. Consequently, we propose an R-D model based on the parameters derived from the border blocks and a block-based shape complexity for the video object. The computational complexity is much lower than other existing methods. Experimental results show that it can accurately predict the bit rate and distortion of the binary shape for rate control purposes.
27700138
Improved single-video-object rate control for MPEG-4
Rate control plays a central role in constant bit-rate video coding applications using MPEG-4. This paper considers single-video-object rate control for MPEG-4 and presents a new rate-control algorithm based on the quadratic rate-distortion (R-D) model. The major innovations are a novel constraint for the least-mean-square estimation of the model parameters of the R-D function, a new measure for the encoding complexity, novel quantizer control and an efficient frame skipping strategy. Simulation results show that the MPEG-4 coder, using the proposed algorithm, can achieve a higher PSNR than a coder using the conventional rate-control algorithm.
7066307
Measuring Network-Aware Worm Spreading Ability
This work investigates three aspects: (a) a network vulnerability as the non-uniform vulnerable-host distribution, (b) threats, i.e., intelligent worms that exploit such a vulnerability, and (c) defense, i.e., challenges for fighting the threats. We first study five data sets and observe consistent clustered vulnerable-host distributions. We then present a new metric, referred to as the non-uniformity factor, which quantifies the unevenness of a vulnerable-host distribution. This metric is essentially the Renyi information entropy and better characterizes the non-uniformity of a distribution than the Shannon entropy. We then analytically and empirically measure the infection rate and the propagation speed of network-aware worms. We show that a representative network-aware worm can increase the spreading speed by exactly or nearly a non-uniformity factor when compared to a random-scanning worm at the early stage of worm propagation. This implies that when a worm exploits an uneven vulnerable-host distribution as a network-wide vulnerability, the Internet can be infected much more rapidly. Furthermore, we analyze the effectiveness of defense strategies on the spread of network-aware worms. Our results demonstrate that counteracting network-aware worms is a significant challenge for the strategies that include host-based defense and IPv6.
8068689
Statistical correlation analysis in image retrieval
A statistical correlation model for image retrieval is proposed. This model captures the semantic relationships among images in a database from simple statistics of user-provided relevance feedback information. It is applied in the post-processing of image retrieval results such that more semantically related images are returned to the user. The algorithm is easy to implement and can be efficiently integrated into an image retrieval system to help improve the retrieval performance. Preliminary experimental results on a database of 100,000 images show that the proposed model could improve image retrieval performance for both content- and text-based queries.
432736
ReCoM: reinforcement clustering of multi-type interrelated data objects
Most existing clustering algorithms cluster highly related data objects such as Web pages and Web users separately. The interrelation among different types of data objects is either not considered, or represented by a static feature space and treated in the same ways as other attributes of the objects. In this paper, we propose a novel clustering approach for clustering multi-type interrelated data objects, ReCoM (Reinforcement Clustering of Multi-type Interrelated data objects). Under this approach, relationships among data objects are used to improve the cluster quality of interrelated data objects through an iterative reinforcement clustering process. At the same time, the link structure derived from relationships of the interrelated data objects is used to differentiate the importance of objects and the learned importance is also used in the clustering process to further improve the clustering results. Experimental results show that the proposed approach not only effectively overcomes the problem of data sparseness caused by the high dimensional relationship space but also significantly improves the clustering accuracy.
1777247
Web-page classification through summarization
Web-page classification is much more difficult than pure-text classification due to a large variety of noisy information embedded in Web pages. In this paper, we propose a new Web-page classification algorithm based on Web summarization for improving the accuracy. We first give empirical evidence that ideal Web-page summaries generated by human editors can indeed improve the performance of Web-page classification algorithms. We then propose a new Web summarization-based classification algorithm and evaluate it along with several other state-of-the-art text summarization algorithms on the LookSmart Web directory. Experimental results show that our proposed summarization-based classification algorithm achieves an approximately 8.8% improvement as compared to pure-text-based classification algorithm. We further introduce an ensemble classifier using the improved summarization algorithm and show that it achieves about 12.9% improvement over pure-text based methods.
11939257
Building a web thesaurus from web link structure
Thesaurus has been widely used in many applications, including information retrieval, natural language processing, and question answering. In this paper, we propose a novel approach to automatically constructing a domain-specific thesaurus from the Web using link structure information. The proposed approach is able to identify new terms and reflect the latest relationship between terms as the Web evolves. First, a set of high quality and representative websites of a specific domain is selected. After filtering out navigational links, link analysis is applied to each website to obtain its content structure. Finally, the thesaurus is constructed by merging the content structures of the selected websites. The experimental results on automatic query expansion based on our constructed thesaurus show 20% improvement in search precision compared to the baseline.