23-04-2011, 03:24 PM
Presented by:
NAGESHA.G
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Introduction
Traditionally image processing software in mobile devices was designed based on the centralized computing mode.
Due to resource limitation this had problems running in mobile devices.
To overcome this, a solution is developed here by adopting web service-based image processing method.
Web service-based image processing method can effectively solve the resource bottle-neck that traditional image processing software had.
Web Service
Web service adopts a Service-Oriented Architecture(SOA) as shown in the above figure.
Service Providers are the owners of the services. They create/maintain and publish the services in the service registry center.
Service Requesters are the enterprises, organization and individuals that have needs of specific service functions. Service requesters can discover services and access the information services, binding in the registry center.
Service Registry Center is a database that can store service description information. In the registry center, service providers publish services, service requesters can discover and access binding information.
Web service as a newly distributed computing model has the following advantages
Excellent Encapsulation
Loose coupling
Standard protocols
Integration capabilities
It can make full use of heterogeneous network computing resources and realize resource sharing effectively.
Considering the several advantages of web service, the technology can be applied to mobile devices in order to improve image processing efficiency.
Design and Implementation of Web Service-Based Image Processing System
Experiment and Discussion
Design and Implementation of Web Service-Based Image Processing System
Image processing service can be defined as follows
Definition 1. Image Processing Service Request can be described as following two-tuple
R= <K,C>
Definition 2. An Image Processing Web Service can be defined as following four-tuple
WS=<R, Q, Res, Qos>
WS denotes image processing services names.
R denotes image processing requests.
Q denotes description of specific image processing services.
Res denotes image processing services’ memory usage.
Qos denotes WS’s quality of services. Generally we consider response time to
R denotes image processing requests.
K denotes R’s parameters set ie.., set K is byte array of image files.
C denotes R’s parameters constraint set ie.., the constraints of image file’s format.
Web Service-Based Image Processing Model
Architecture of the system
Constuction of the system
Implementation of the system
Key algorithm-WSDA in system
Experiment and discussion
Conclusion