{"id":316640,"date":"2016-11-06T19:43:19","date_gmt":"2016-11-07T03:43:19","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-research-item&p=316640"},"modified":"2018-10-16T20:05:32","modified_gmt":"2018-10-17T03:05:32","slug":"compression-rendering-concentric-mosaics-reference-block-codec-rbc","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/compression-rendering-concentric-mosaics-reference-block-codec-rbc\/","title":{"rendered":"Compression and Rendering of Concentric Mosaics with Reference Block Codec (RBC)"},"content":{"rendered":"

Concentric mosaics have the ability to quickly capture a complete 3D view of a realistic environment and to enable a user to wander freely in the environment. However, the data amount of the concentric mosaics is huge. In this paper, we propose an algorithm to compress the concentric mosaic image array through motion compensation and residue coding, which we called reference block codec (RBC). A two-level index table is embedded in the compressed bitstream for random access. During the rendering, the entire compressed concentric mosaic scene is not fully expanded at any time. In stead, only the contents necessary to render the current view are decoded in real time. We denote such rendering scheme as just-in-time (JIT) rendering. Four decoder caches are implemented to speed up the rendering.<\/p>\n","protected":false},"excerpt":{"rendered":"

Concentric mosaics have the ability to quickly capture a complete 3D view of a realistic environment and to enable a user to wander freely in the environment. However, the data amount of the concentric mosaics is huge. In this paper, we propose an algorithm to compress the concentric mosaic image array through motion compensation and […]<\/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":[13560],"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-316640","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-programming-languages-software-engineering","msr-locale-en_us"],"msr_publishername":"International Society for Optics and Photonics","msr_edition":"Visual Communications and Image Processing 2000","msr_affiliation":"","msr_published_date":"2000-06-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"316643","msr_publicationurl":"","msr_doi":"10.1117\/12.386665","msr_publication_uploader":[{"type":"file","title":"vcip00_chazhang","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/11\/vcip00_ChaZhang.pdf","id":316643,"label_id":0},{"type":"doi","title":"10.1117\/12.386665","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"user_nicename","value":"chazhang","user_id":31379,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=chazhang"},{"type":"text","value":"J. 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