Mapping and inverse mapping relation in image compression using neural network
Date of Publication
1993
Document Type
Master's Thesis
Degree Name
Master of Science in Electronics and Communications Engineering
Subject Categories
Engineering
College
Gokongwei College of Engineering
Department/Unit
Electronics and Communications Engineering
Thesis Adviser
Dr. Felicito Caluyo
Defense Panel Chair
Rudy Lim
Defense Panel Member
Roberto Caguinguin
Aliento Estalilla
Abstract/Summary
Image Compression involves converting an image into a new representation that uses a similar number of bits. The resulting representation can be used to reconstruct the original image without sacrificing the quality of the image. There are several techniques in image compression but those techniques depend on the application. This research will present a new technique in image compression for gray levels using a neural network. The 64 by L by 64 and 128 by L by 128 neural network architectures will be used to figure out the most appropriate mapping and inverse mapping relation for a particular application. Simulation is done in a personal computer to achieve at most an 8 to 1 compression ratio.
Abstract Format
html
Language
English
Format
Accession Number
TG02220
Shelf Location
Archives, The Learning Commons, 12F Henry Sy Sr. Hall
Physical Description
1 v. (various pagings)
Keywords
Mappings (Mathematics); Neural network; Image transmission; Data compression (Telecommunication); Algorithms
Recommended Citation
Sybingco, E. (1993). Mapping and inverse mapping relation in image compression using neural network. Retrieved from https://animorepository.dlsu.edu.ph/etd_masteral/1540
