{"id":{"repo_id":"unlv","oai_identifier":"oai:oasis.library.unlv.edu:rtds-1296"},"canonical_url":"https://search.dev.ndltd.org/etd/unlv/oai:oasis.library.unlv.edu:rtds-1296","repository":{"repo_id":"unlv","name":"University of Nevada - Las Vegas","base_url":"https://oasis.library.unlv.edu/do/oai/"},"display":{"title":"Application of multirate digital signal processing to image compression","abstract":"With the increasing emphasis on digital communication and digital processing of images and video, image compression is drawing considerable interest as a means of reducing computer storage and communication channels bandwidth requirements. This thesis presents a method for the compression of grayscale images which is based on the multirate digital signal processing system. The input image spectrum is decomposed into octave wide subbands by critically resampling and filtering the image using separable FIR digital filters. These filters are chosen to satisfy the perfect reconstruction requirement. Simulation results on rectangularly sampled images (including a text image) are presented. Then, the algorithm is applied to the hexagonally resampled images and the results show a slight increase in the compression efficiency. Comparing the results against the standard (JPEG), indicate that this method does not have the \"blocking effect\" of JPEG and it preserves the edges even in the presence of high noise level.","abstract_html":"With the increasing emphasis on digital communication and digital processing of images and video, image compression is drawing considerable interest as a means of reducing computer storage and communication channels bandwidth requirements. This thesis presents a method for the compression of grayscale images which is based on the multirate digital signal processing system. The input image spectrum is decomposed into octave wide subbands by critically resampling and filtering the image using separable FIR digital filters. These filters are chosen to satisfy the perfect reconstruction requirement. Simulation results on rectangularly sampled images (including a text image) are presented. Then, the algorithm is applied to the hexagonally resampled images and the results show a slight increase in the compression efficiency. 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