Abstract
ABSTRACT The Vector Quantization technique, employed in this paper is based on an upgraded Linde–Buzo–Gray (LBG) with a DCT method to store the image data. The code-books are optimized using a unique approach called hybrid optimization. Here, the database image is divided into a series of blocks called vectors. Then, a suitable codeword is chosen as the closest approximation of the input vector. The encoder sends the compressed stream, i.e. indices of these vectors to the decoder. The decoder subsequently decodes the index to obtain the compressed vector. Moreover, the Flower Pollination Adapted Rider Optimization (FPARA) model is used to improve the codebook for providing best image compression result. The optimization of code books is done to lower the summation of compression ratio and the error difference between the original and decompressed images. Therefore, the adopted approach is computed to the traditional schemes, and the simulation outcomes are validated.
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.
