{"id":161769,"date":"2011-10-01T00:00:00","date_gmt":"2011-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/low-complexity-near-lossless-coding-of-depth-maps-from-kinect-like-depth-cameras\/"},"modified":"2018-10-16T19:58:26","modified_gmt":"2018-10-17T02:58:26","slug":"low-complexity-near-lossless-coding-of-depth-maps-from-kinect-like-depth-cameras","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/low-complexity-near-lossless-coding-of-depth-maps-from-kinect-like-depth-cameras\/","title":{"rendered":"Low-Complexity, Near-Lossless Coding of Depth Maps from Kinect-Like Depth Cameras"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Depth cameras are gaining interest rapidly in the market as depth plus RGB is being used for a variety of applications ranging from foreground\/background segmentation, face tracking, activity detection, and free viewpoint video rendering. In this paper, we present a low-complexity, near-lossless codec for coding depth maps. This coding requires no buffering of video frames, is table-less, can encode or decode a frame in close to 5ms with little code optimization, and provides between 7:1 to 16:1 compression ratio for near-lossless coding of 16-bit depth maps generated by the Kinect camera.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Depth cameras are gaining interest rapidly in the market as depth plus RGB is being used for a variety of applications ranging from foreground\/background segmentation, face tracking, activity detection, and free viewpoint video rendering. In this paper, we present a low-complexity, near-lossless codec for coding depth maps. This coding requires no buffering of video frames, [&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":"","msr-author-ordering":[{"type":"user_nicename","value":"sanjeevm"},{"type":"user_nicename","value":"zhang"},{"type":"user_nicename","value":"qincai"},{"type":"user_nicename","value":"chazhang"},{"type":"user_nicename","value":"pachou"}],"msr_publishername":"IEEE","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of MMSP","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"\u00a9 2011 IEEE. Personal use of this material is permitted. However, permission to reprint\/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.","msr_conference_name":"Proceedings of MMSP","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Philip A. 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