Abstract:

Current e-learning systems use large amounts of multimedia data for the presentation of educational content. An important part is image data. Reducing the memory demands of image data will lower network load and increase the speed of the system. It is now particularly important in the case of m-learning when users often access to educational content via a slow data network. The article introduces a new compression algorithms and methods that can be used to reduce memory demands visual materials in e-learning systems. The described methods significantly reduce image data (and thus increase the speed of data transfer and provides a smaller network load), while the reduction in image quality is minimal. The paper presents the results of comparative analysis finding the best data volume reduction method of images used in e-learning materials. Within the comparative analysis a visual quality of compressed image determined by subjective perception of users depending on size of images compression was evaluated.

Keywords:

Jpeg, png, mozjpeg, m-learning, e-learning, dithering, lossy compression, pngquant.