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PRODID:IEEE vTools.Events//EN
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TZID:Pacific/Auckland
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DTSTART:20260927T030000
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BEGIN:VEVENT
DTSTAMP:20260810T075502Z
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DTSTART;TZID=Pacific/Auckland:20260807T120000
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DESCRIPTION:We will review the history of video encoding\, covering aspects
  of video codecs and quality metrics that have been exploited to optimize 
 video delivery. Next\, we will discuss the roots of convex optimization in
  video coding\, connecting statistical multiplexing\, rate-distortion opti
 mization and how these led to the dynamic optimization framework. We will 
 also discuss the complexity of video encoding (and quality metrics) and pr
 esent the concept of a three-part tradeoff among bitrate-quality-complexit
 y\, as well as the important role this tradeoff plays in massive video pro
 cessing systems. We will analyze software (SW) and hardware (HW) transcodi
 ng pipelines and conclude with an array of open research problems\, includ
 ing the use of ML/AI in video coding and video quality metrics.\n\nSpeaker
 (s): Ioannis \n\nRoom: Planning 619 Seminar Room (45) \, Bldg: 421E-619\, 
 The University of Auckland. \,  26 SYMONDS ST\, Auckland\, North Island\, 
 New Zealand\, 1010\, Virtual: https://events.vtools.ieee.org/m/570225
LOCATION:Room: Planning 619 Seminar Room (45) \, Bldg: 421E-619\, The Unive
 rsity of Auckland. \,  26 SYMONDS ST\, Auckland\, North Island\, New Zeala
 nd\, 1010\, Virtual: https://events.vtools.ieee.org/m/570225
ORGANIZER:mathew@nzse.ac.nz
SEQUENCE:18
SUMMARY:Video Quality Optimization in Adaptive Streaming - from principles 
 to popular applications
URL;VALUE=URI:https://events.vtools.ieee.org/m/570225
X-ALT-DESC:Description: <br /><p>We will review the history of video encodi
 ng\, covering aspects of video codecs and quality metrics that have been e
 xploited to optimize video delivery. Next\, we will discuss the roots of c
 onvex optimization in video coding\, connecting statistical multiplexing\,
  rate-distortion optimization and how these led to the dynamic optimizatio
 n framework. We will also discuss the complexity of video encoding (and qu
 ality metrics) and present the concept of a three-part tradeoff among bitr
 ate-quality-complexity\, as well as the important role this tradeoff plays
  in massive video processing systems. We will analyze software (SW) and ha
 rdware (HW) transcoding pipelines and conclude with an array of open resea
 rch problems\, including the use of ML/AI in video coding and video qualit
 y metrics.</p>
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