29712526
Three-sided side match finite-state vector quantization
Several low bit-rate still-image compression methods have been presented, such as SPHIT, hybrid VQ, and the Wu-Chen (see Proc. IEEE ICASSP, 1997) method. In particular, the image "Lena" can be compressed using less than 0.15 bpp at 31.4 dB or higher. These methods exercise the analysis techniques (wavelet or subband) before distributing the bit rate to each piece of an image, thus the dilemma between bit rate and distortion can be solved. In this paper, we propose a simple but comparable method that adopts the technique of side match VQ only. The side match vector quantization (SMVQ) is an effective VQ coding scheme at low bit-rate. The conventional side match (two-sided) VQ utilizes the codeword information of two neighboring blocks to predict the state codebook of an input vector. We propose a hierarchical three-sided side match finite-state vector quantization (HTSMVQ) method that can: (1) make the state codebook size as small as possible-the size is reduced to one if the prediction can perform perfectly; (2) improve the prediction quality for edge blocks; and (3) regularly refresh the codewords to alleviate the error propagation of side match. In the simulation results, the image "Lena" can be coded with a PSNR 34.682 dB at 0.25 bpp. It is better than SPIHT, EZW, FSSQ and hybrid VQ with 34.1, 33.17, 33.1, and 33.7 dB, respectively. At a bit rate lower than 0.15 bpp, only the enhanced version of EZW performs better than our method, at about 0.14 dB.
13282723
A cascading MRVQ-DCT scheme for the compression of sequence images
In this correspondence, a mean residual vector quantizer (MRVQ) is superimposed on the motion estimator for improving the intraframe coding quality of MPEG. The proposed scheme performs an MRVQ before executing the discrete cosine transformation (DCT) for the purpose of curing the DCT intrinsic trouble of blocking effect at low bit rate.
28811627
Recognizing depth-first-search trees in parallel
Consider that T. is a given spanning tree of an undirected graph G which contains a vertices and m (/spl ges/n-1) edges. In this paper, we propose an O(m/p+log m)-time parallel algorithm using p processors on the EREW PRAM model to determine whether T is a depth-first-search tree of G. Our algorithm is optimal in time complexity and speed-up.<<ETX>>
1269749
Complexity-adaptive search algorithm for block motion estimation
We propose a complexity-adaptive fast block matching algorithm design strategy that allows the user to terminate the algorithm at any target computational complexity. Two complexity-adaptive techniques, frame level complexity allocation and block level complexity allocation, are developed to approach the global complexity-distortion optimization A buffer control strategy is proposed to dynamically adjust the target complexity of one frame. The predictive complexity-distortion benefit list (PCDB list) technique is employed to allocate the target complexity into each block. By these techniques, we modify the full-search block matching, three-step search, new three-step search, and four-step search to the complexity-adaptive algorithms. Experimental results show that the complexity-adaptive algorithms could achieve better performance than traditional fixed algorithms from the viewpoint of complexity-distoftion optimization.
207320252
An efficient kernel discriminant analysis method
Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, which is able to effectively address the two problems by using regularization and subspace decomposition techniques. Experiments performed on real face databases indicate that the proposed method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel principal component analysis and kernel linear discriminant analysis, at a significantly reduced computational cost.
2544976
Generalized 2D principal component analysis for face image representation and recognition
In the tasks of image representation, recognition and retrieval, a 2D image is usually transformed into a 1D long vector and modelled as a point in a high-dimensional vector space. This vector-space model brings up much convenience and many advantages. However, it also leads to some problems such as the Curse of Dimensionality dilemma and Small Sample Size problem, and thus produces us a series of challenges, for example, how to deal with the problem of numerical instability in image recognition, how to improve the accuracy and meantime to lower down the computational complexity and storage requirement in image retrieval, and how to enhance the image quality and meanwhile to reduce the transmission time in image transmission, etc. In this paper, these problems are solved, to some extent, by the proposed Generalized 2D Principal Component Analysis (G2DPCA). G2DPCA overcomes the limitations of the recently proposed 2DPCA (Yang et al., 2004) from the following aspects: (1) the essence of 2DPCA is clarified and the theoretical proof why 2DPCA is better than Principal Component Analysis (PCA) is given; (2) 2DPCA often needs much more coefficients than PCA in representing an image. In this work, a Bilateral-projection-based 2DPCA (B2DPCA) is proposed to remedy this drawback; (3) a Kernel-based 2DPCA (K2DPCA) scheme is developed and the relationship between K2DPCA and KPCA (Scholkopf et al., 1998) is explored. Experimental results in face image representation and recognition show the excellent performance of G2DPCA.
52854920
Integrated architectural modeling and analysis for high-assurance command and control system design
A Real-Time Architectural Specification (RAS) approach and its application to command and control (C2) systems are presented. Our objective is to establish a formal foundation that will enable us to integrate existing rich but fragmented formal techniques for system specification and verification into a practical and scaleable formal engineering method to support the design and development of highly reliable real-time distributed systems. The contribution of RAS is twofold: First, it provides a formal system that integrates system's timing requirements and requirements propagation into the process of architectural modeling and design in such a way that allows us to systematically enforce that the requirements are met in every step of the design process. Second, it offers an incremental and more scaleable approach for design modeling. These two features together make RAS a suitable model for the design of C2 systems. We further present an incremental method for verifying timing properties of an RAS model that helps to reduce the complexity of analysis both at a given design level and across different design levels.
15400274
Cluster Monte Carlo Algorithms and Their Applications
We introduce the Markov chain Monte Carlo method and review the background of the cluster algorithms in statistical physics. One of the first such successful algorithm was developed by Swendsen and Wang eight years ago. In contrast to the local algorithms, cluster algorithms update dynamical variables in a global fashion. Therefore, large changes are made in a single step. The method is very efficient, especially near the critical point of a second-order phase transition. Studies of various statistical mechanics models and some generalizations of the algorithm will be briefly reviewed. We mention applications in other fields, especially in imaging processing.
2380687
Packet classification using multidimensional cutting
This paper introduces a classification algorithm called phHyperCuts. Like the previously best known algorithm, HiCuts, HyperCuts is based on a decision tree structure. Unlike HiCuts, however, in which each node in the decision tree represents a hyperplane, each node in the HyperCuts decision tree represents a k--dimensional hypercube. Using this extra degree of freedom and a new set of heuristics to find optimal hypercubes for a given amount of storage, HyperCuts can provide an order of magnitude improvement over existing classification algorithms. HyperCuts uses 2 to 10 times less memory than HiCuts optimized for memory, while the worst case search time of HyperCuts is 50--500% better than that of HiCuts optimized for speed. Compared with another recent scheme, EGT-PC, HyperCuts uses 1.8--7 times less memory space while the worst case search time is up to 5 times smaller. More importantly, unlike EGT-PC, HyperCuts can be fully pipelined to provide one classification result every packet arrival time, and also allows fast updates.