{"id":161973,"date":"1996-10-01T00:00:00","date_gmt":"1996-10-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/motion-transforms-for-video-coding\/"},"modified":"2018-10-16T20:05:05","modified_gmt":"2018-10-17T03:05:05","slug":"motion-transforms-for-video-coding","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/motion-transforms-for-video-coding\/","title":{"rendered":"Motion Transforms for Video Coding"},"content":{"rendered":"
Video coding standards use motion compensated prediction to reduce temporal redundancies. In general this is performed by computing motion vectors for predefined regions in the image (rectangular blocks), and transmitting these vectors as side-information. The Motion Transform (MT) provides a new motion compensation technique that does not require the transmission of motion vectors and yet, performs as well as forward motion estimation for many image sequences. In the MT, motion information is obtained in a bottom-up hierarchical fashion using a two-dimensional non-linear filter bank. In this paper we elaborate on MT implementation details and discuss a new variation of the MT. Results obtained using MTs on typical image sequences are compared to results for the full search block matching algorithm (BMA).<\/p>\n<\/div>\n
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Video coding standards use motion compensated prediction to reduce temporal redundancies. In general this is performed by computing motion vectors for predefined regions in the image (rectangular blocks), and transmitting these vectors as side-information. The Motion Transform (MT) provides a new motion compensation technique that does not require the transmission of motion vectors and yet, […]<\/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-161973","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_publishername":"IEEE SPS","msr_edition":"ICIP","msr_affiliation":"","msr_published_date":"1996-10-01","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":"211596","msr_publicationurl":"","msr_doi":"","msr_publication_uploader":[{"type":"file","title":"MT_video_icip1996.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/MT_video_icip1996.pdf","id":211596,"label_id":0}],"msr_related_uploader":"","msr_attachments":[{"id":211596,"url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/02\/MT_video_icip1996.pdf"}],"msr-author-ordering":[{"type":"user_nicename","value":"dinei","user_id":31633,"rest_url":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/microsoft-research\/v1\/researchers?person=dinei"},{"type":"text","value":"Robert M. 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