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  • 1.
    Halldén, Albin
    et al.
    Malmö högskola, Faculty of Technology and Society (TS).
    Schönemann, Madeleine
    Malmö högskola, Faculty of Technology and Society (TS).
    Adaptiv bildladdning i en kontextmedveten webbtjänst2014Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Today, information on the web is consumed via a variety of heterogeneous devices. Factors, such as network connection and screen resolution, affects which image that is the most suitable to deliver to the client. An image in its original condition, in a technically limited device, takes a long time to download and requires a large amount of data. Since the number of devices browsing the internet via mobile networks are expected to increase, a solution for adaptive image loading is needed. The aim of this thesis is to explore whether a web service, consisting of a client and a server, can determine the best suited image that should be delivered to the client. This is based on the client’s current network connection and screen resolution. A device with a lower screen resolution and a slower network connection requires an image of lower quality and lower resolution. Thus, the download time can be shortened and the data volume reduced, contributing to improved user experience. Our adaptive solution is based on two measurements – the width of the client’s browser window and the latency between the client and the server – using javascript. These para- meters are the basis for the scaling of the size and quality which applies to the image. The image is provided to the client by one of the two delivery methods: “predefined images”, where several different versions of the image are stored on the server, and “dynamic images”, where the images are rendered on the server by the gd library in php, based on the original image. Three types of adaptive image loading – quality adaptation, size adaptation and a combination of both, are investigated considering delivery time and the amount of data delivered. These are then evaluated in relation to the base case consisting of the original images. Using some type of adaptation method is in 14 out of 15 cases better than simply delivering the original images. The best results are given by the combined adaption method on devices with smaller screen resolutions and slower network connections, but is also beneficial for devices with medium speed connections and devices that support higher screen resolutions. Both predefined and dynamic delivery methods shows good results, but since the dynamic delivery method’s scalability with multiple concurrent clients is not known, it is recommended to use predefined images.

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