Abstract
Laplacian and generalized Gaussian data arise in the transform and subband coding of images. This paper describes a method of rotating independent, identically distributed (i.i.d.) Laplacian-like data in multiple dimensions to significantly improve the overload characteristics for quantization. The rotation is motivated by the geometry of the Laplacian probability distribution, and can be achieved with only additions and subtractions using a Walsh-Hadamard transform. Its theoretical and simulated results for scalar, lattice, and polar quantization are presented in this paper, followed by a direct application to image compression. We show that rotating the image data before quantization not only improves compression performance, but also increases robustness to the channel noise and deep fades often encountered in wireless communication.
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.
