Abstract
A new fast search algorithm for vector quantization using the mean of image vectors is proposed. The codevectors are sorted according to their component means, and the search for the codevector having the minimum Euclidean-distance to a given input vector starts with the one having the minimum mean-distance to it, making use of our observation that the two codevectors are close to each other in most real images. The search is then made to terminate as soon as a simple yet novel test reports that any remaining vector in the codebook should have a larger Euclidean distance. Simulations show that the number of calculations can be reduced to as low as a fourth the number achievable by an algorithm known as the partial distance method.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
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 callMore From: IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing
Disclaimer: 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.
