{"id":161970,"date":"1994-01-01T00:00:00","date_gmt":"1994-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-non-expansive-pyramidal-morphological-image-coder\/"},"modified":"2018-10-16T20:04:54","modified_gmt":"2018-10-17T03:04:54","slug":"a-non-expansive-pyramidal-morphological-image-coder","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-non-expansive-pyramidal-morphological-image-coder\/","title":{"rendered":"A Non-Expansive Pyramidal Morphological Image Coder"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Pyramid Image Coding is a natural coding scheme for applications where progressive transmission is desired. In this kind of coder, versions of the original image at several resolution levels are formed by successive filtering and subsampling. Then, beginning from the coarsest image, the image is used to produce an estimate for the next (higher resolution) level and the error is coded and transmitted. While expansive pyramids (e.g., Burt\u2019s Laplacian Pyramid) are usually less efficient, non-expansive pyramids tend to produce ringing (e.g., Subband\/Wavelet) and\/or blocking (e.g., DCT). In this paper we introduce a non-expansive pyramid that does not. produce ringing or blocking effects. Instead, the main artifact is texture removal. The simulations have produced images with entropies in the range of .2 to 1.5 bpp, with SNR figures similar to or better than JPEG at equivalent rates. The proposed coder has several attractive features, including 8 bit integer operations only, a perfect reconstruction mode, progressive transmission and an interesting progressive computational property.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Pyramid Image Coding is a natural coding scheme for applications where progressive transmission is desired. In this kind of coder, versions of the original image at several resolution levels are formed by successive filtering and subsampling. Then, beginning from the coarsest image, the image is used to produce an estimate for the next (higher resolution) [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13551],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-161970","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_publishername":"IEEE 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