Abstract
21st century is a digital information age, different high technologies emerge in succession. Internet technology develops rapidly and is widely used in various industries and fields now. The popularity of 5G mobile communication technology, interactive multimedia, and remote sensing technology results in massive growth of image data. In recent years, image acquisition equipment has been widely used in production and life and the acquisition resolution has got continuous improvement as well. But how to store and transmit large numbers of image data has become big issues to production and life. How to efficiently compress, store and transmit images without destroying original information to be transmitted by the images is currently a major focus in the field of computer vision. The thesis mainly studies the methods of compressing images in time series. Firstly several basic image compression methods are introduced in details. Secondly traditional JEPG static image compression algorithm is fully analyzed. Thirdly time series are added on static image compression algorithm and groups of highly similar pictures are compressed in time series with video formats to eliminate redundant time for better efficiency optimization. Verifying through experiments and comparing with traditional compression algorithm, we get a conclusion that compressing images in time series can improve compression efficiency a lot.
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Chen, J. et al. (2021). Image Compression Algorithm Based on Time Series. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_58
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DOI: https://doi.org/10.1007/978-3-030-78609-0_58
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