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BEGIN:VEVENT
UID:77b60faa769c34c6b6a6478faa6e5b148887b900@swoogo.com
DTSTAMP:20260812T233023Z
DESCRIPTION:In the 20-year history of video compression for broadcast TV se
 rvices\, there has been a major codec standard created every decade. This 
 began with the release of MPEG-2 in 1995\, then came AVC in 2005\, and rec
 ently HEVC in 2015. Each codec took many years to develop\, with teams of 
 engineers making consistent but slow improvements. Then after each standar
 d issue\, it would take more time to reach its full potential as more adva
 nces are made. \n\nWith the introduction of this new and disruptive techno
 logy\, Artificial Intelligence (AI) is driving the next frontier of video 
 compression enhancements with the promise of faster advancements. AI is be
 ing used to improve several key areas including consistent higher video qu
 ality (VQ) at any bit rate\, higher density using less computing resources
 \, and improving the quality of experiences (QoE). This can be accomplishe
 d with AI through Dynamic Encoding Style (DES)\, Dynamic Resolution Encodi
 ng (DRE)\, and Dynamic Frame rate Encoding (DFE). \n\nThis paper will pres
 ent three examples of AI applied to video encoding to optimize broadcast a
 nd OTT content delivery through these methods and explore the operational 
 and end-user benefits enabled by AI and machine learning. Additionally\, i
 t will provide measurement for the applications that are presented and add
 ress the possible future evolutions of AI for video compression.
DTSTART:20191021T170000Z
DTEND:20191021T173000Z
LAST-MODIFIED:20260812T233023Z
LOCATION:San Francisco Room
SEQUENCE:0
STATUS:CONFIRMED
SUMMARY:How AI Technology is Dramatically Improving Video Compression for B
 roadcast and OTT Content Delivery
TRANSP:OPAQUE
X-ALT-DESC;FMTTYPE=text/html:In the 20-year history of video compression fo
 r broadcast TV services\, there has been a major codec standard created ev
 ery decade. This began with the release of MPEG-2 in 1995\, then came AVC 
 in 2005\, and recently HEVC in 2015. Each codec took many years to develop
 \, with teams of engineers making consistent but slow improvements. Then a
 fter each standard issue\, it would take more time to reach its full poten
 tial as more advances are made. <br /><br />\nWith the introduction of thi
 s new and disruptive technology\, Artificial Intelligence (AI) is driving 
 the next frontier of video compression enhancements with the promise of fa
 ster advancements. AI is being used to improve several key areas including
  consistent higher video quality (VQ) at any bit rate\, higher density usi
 ng less computing resources\, and improving the quality of experiences (Qo
 E). This can be accomplished with AI through Dynamic Encoding Style (DES)\
 , Dynamic Resolution Encoding (DRE)\, and Dynamic Frame rate Encoding (DFE
 ). <br /><br />\nThis paper will present three examples of AI applied to v
 ideo encoding to optimize broadcast and OTT content delivery through these
  methods and explore the operational and end-user benefits enabled by AI a
 nd machine learning. Additionally\, it will provide measurement for the ap
 plications that are presented and address the possible future evolutions o
 f AI for video compression.
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