Abstract
Image compression, in the present context of heavy network traffic, is going through major research and development. Traditional entropy coding techniques, for their high computational cost is becoming inappropriate. In this paper a novel near-lossless image compression algorithm had been proposed which follows simple tree encoding and prediction method for image encoding. The prediction technique uses a simple summation process to retrieve image data from residual samples. The algorithm had been tested on several gray-scale standard test images, both continuous and discreet tone, and had produced compression comparable to other state-of-the-art compression algorithms. The output compressed file sizes had shown that they are independent of image data, and depends only on the resolution of the image, an unique property that can exploited for networking bandwidth utilization.
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Banerjee, S., Chakroborty, D. (2011). Fast Near-Lossless Image Compression with Tree Coding having Predictable Output Compression Size. In: Das, V.V., Thomas, G., Lumban Gaol, F. (eds) Information Technology and Mobile Communication. AIM 2011. Communications in Computer and Information Science, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20573-6_7
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DOI: https://doi.org/10.1007/978-3-642-20573-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20572-9
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