295516
A fuzzy metric for image quality assessment
Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L/sup 2/-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with human visual perception in many cases. In this paper, a fuzzy image metric (FIM) is defined based on M. Sugeno's (1974) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgement procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using PSNR.
7606399
A fuzzy image metric with application to fractal coding
Image quality assessment is an important issue addressed in various image processing applications such as image/video compression and image reconstruction. The peak signal-to-noise ratio (PSNR) with the L(2)-metric is commonly used in objective image quality assessment. However, the measure does not agree very well with the human visual perception in many cases. A fuzzy image metric (FIM) is defined based on Sugeno's (1977) fuzzy integral. This new objective image metric, which is to some extent a proper evaluation from the viewpoint of the judgment procedure, is closely approximates the subjective mean opinion score (MOS) with a correlation coefficient of about 0.94, as compared to 0.82 obtained using the PSNR. Compared to the L(2)-metric, we demonstrate that a better performance can be achieved in fractal coding by using the proposed FIM.
37994970
Image coding quality assessment using fuzzy integrals with a three-component image model
Based on importance measures and fuzzy integrals, a new assessment method for image coding quality is presented in this paper. The proposed assessment is based on two subevaluations. In the first subevaluation, errors on edges, textures, and flat regions are computed individually. The errors are then assessed using an assessment function. A global evaluation with Sugeno fuzzy integral is then obtained based on the importance measure of edge, texture, and flat region. In the second subevaluation, an importance measure is first established depending on the types of regions where errors occur, a subtle evaluation is then obtained using Sugeno fuzzy integral on all pixels of the image. A final evaluation is obtained based on the two subevaluations. Experimental results show that this new image quality assessment closely approximates human subjective tests such as mean opinion score with a high correlation coefficient of 0.963, which is a significant improvement over peak signal-to-noise ratio, picture quality scale, and weighted mean square error, three other image coding quality assessment methods, which have the correlation coefficients of 0.821, 0.875, and 0.716, respectively.
42670703
A Latent Image Semantic Indexing Scheme for Image Retrieval on the Web
In this paper, we present a novel latent image semantic indexing scheme for efficient retrieval of WWW images. We present a hierarchical image semantic structure called HIST, which captures image semantics in an ontology tree and visual features in a set of specific semantic domains. The query algorithm works in two phases. First, the ontology is used for quickly locating the relevant semantic domains. Second, within each semantic domain, the visual features are extracted, and similarity techniques are exploited to break the “dimensionality curse”. The target images can then be efficiently retrieved with high precision. The experimental results show that HIST achieves good query performance. Therefore, our method is promising in diverse Web image retrieval.
14149219
Multi-agent coordination: a fuzzy logic based approach
In the past decade, the multiagent systems have appeared as a new promising software paradigm. However, its succesful application is subject to the adoption of effective agent coordination techniques. In this paper, we consider a fuzzy logic based approach to agent coordination. Specifically, a bidding based coordination strategy has been proposed to manage the inter-relations among agents. The strategy is developed under the fuzzy logic based task structure model (FSTS) we proposed. Through using the strategy, agents can effectively coordinate their behaviors without consuming so much communication and computation resources.
7188468
GPGPU supported cooperative acceleration in molecular dynamics
Molecular dynamics simulations have become a significant computational approach to study complicated physical phenomena at the atomic level. Nevertheless, accurate simulations are limited in size and timescale by the available computing resources, which make the simulations very time-consuming. This consequentially leads to tremendous computational requirements. Therefore, the need for speeding up this process is crucial. In this paper, we present a novel implementation to accelerate molecular dynamics simulations with GPGPU (General Purpose Graphics Processing Unit). Our goal is to reduce the total computational time of MD simulations at a very high performance/cost ratio with the introduction of the GPGPU algorithm. This is motivated by their enhanced programmability, attractive cost/performance ratio and incredible growth in speed. To demonstrate that GPGPUs already provide an inexpensive alternative to scientific applications, we have used AMD's Brook+ streaming programming environment to implement a new parallel algorithm. Our experimental results show the novel approach achieves speedup by the factor of fifteen compared to the corresponding sequential implementation.
16833900
Securely Sharing Data in Encrypted Databases
Collaborative design and application integration emphasize the demand for database security. Database encryption is widely adopted to ensure data privacy, which can prevent attacks from both outside intruders and inside malicious users. Current researches on this area are mainly focusing on encryption algorithms, key management and encryption efficiency. However, data sharing nature of database system is usually neglected. In this paper, we propose an access control model, 3S-RBAC, which enables data sharing while guaranteeing privacy. The model has many features: the novel concept of strong permission and weak permission; the hierarchy of database objects and keys; the permission and key inheritance; the binding of keys and permissions. Implementation in OSCAR Secure DBMS shows that the model is flexible, secure, practical, and can be integrated easily into existing enterprise applications
640529
Heuristic Discovery of Role-Based Trust Chains in Peer-to-Peer Networks
Credential chains are needed in trusted peer-to-peer (P2P) applications, where trust delegation must be established between each pair of peers at specific role level. Role-based trust is refined from the coarse-grained trust model used in most P2P reputation systems. This paper offers a novel heuristic-weighting approach to selecting the most likely path to construct a role-based trust chain. We apply history-sensitive heuristics to measure the path complexity and assess the chaining efficiency. We discover successive edges of a trust chain, adaptively, to match with the demands from various P2P applications. New heuristic chaining algorithms are developed for backward, forward, and bi-directional discovery of trust chains. Our heuristic chain discovery scheme shortens the search time, reduces the memory requirement, and enhances the chaining accuracy in scalable P2P networks. Consider a trust graph over N credentials and M distinct role nodes. Our heuristic trust-chain discovery algorithms require O(N2 logN) search time and O(M) memory space, if the secondary heuristics are generated off-line in advance. These are improved from O(N3) search time and O(NM) space required in non-heuristic discovery algorithms by Li, Winsborough, and Mitchell (2003). Our analytical results are verified by extensive simulation experiments over typical classes of role-based trust graphs.