Abstract
Space-filling curves (SFCs) have been widely used in image compression as a way to assist prediction-based schemes by providing a method to map an $$m$$ -dimensional space into a highly correlated unidimensional space. In this paper, we not only analyze the effect of different state-of-the-art curves but also propose a novel scanning procedure applied to the preprocessing stage of an ultraspectral lossy compression architecture. Specifically, we focus on Atmospheric infrared sounder images that are good compression candidates as they account for well over 25 megapixels of information per cube. In addition, we introduce a metric to estimate the efficiency of the different SFCs and present a mathematical model that, based on the Laplacian nature of the pixels, it is used to derive bounds. Finally, to verify the accuracy of this metric, we obtain rate-distortion ratios calculated by applying the corresponding curve to the compression architecture.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
