Digital Media Production

Background, challenges and Opportunities

Digital Media Prodution

© VRT-medialab: onderzoek en innovatie2

Table of Content

• 2004 – Background and Objectives• 2009 – Due Diligence

State of the technology Reconciliation of requirements Objectives and Challenges

– Operational Excellence– Scale up– Media Asset Management

• Additional requirements Broadband distribution Supply chain integration

• Technology trends


2004...


Background – Proof of Concept

medialab 5

Media FileMedia File

Media FileMedia File

Fully fragmented: Random access without using any Hard Disk intelligence

Cylinder

Sector

TracksHeads

SpindleArm

Platters

Arm assembly

Arm pivot

128 KB segment

4/8 KB segment Large segment size-> Higher efficiency Hard Disk

100+ HD’s// Striping:

Hard Disk Model

medialab 6

Cylinder

Sector

TracksHeads

SpindleArm

Platters

Arm assembly

Arm pivot

128 KB segment

4/8 KB segment Large segment size-> Higher efficiency Hard Disk

Full stroke8 KB segments

Full stroke128 KB segments

Full Stroke R/W 8KB – 128 KB Segments

Random8 KB segments

Random128 KB segments

Random R/W 8KB – 128 KB Segments

Inner track

R/W on the Inner Track

Outer track

R/W on the Outer Track

Hard Disk Model

medialab 7

Hard Disk Speed

15 Krpm calculated results10 Krpm calculated results128 KB segments8 KB segmentsTestresults

70 MB/s

50 MB/s

30 MB/s

10 MB/s

Read load types(1)Sequential Read

Outer tracks

(2)Sequential Read

Inner tracks

(3)Random Read

Tot. Fragmented disk128 KB / 8KB segments

(4)Worst case Read

Tot. Fragmented disk128 KB / 8KB segments

15Krpm – 10 K rpm 15Krpm – 10 K rpm 15Krpm – 10 K rpm 15Krpm – 10 K rpm

78.2 MB/s

71.5 MB/s

50.9 MB/s

39.4 MB/s

16.8 MB/s12.8 MB/s

0.91 MB/s 0.65 MB/s

12.2 MB/s8.9 MB/s

72.9 MB/s

66.7 MB/s

47.6 MB/s

35.4 MB/s

14.8 MB/s9.1 MB/s

0.92 MB/s 0.62 MB/s

DiskThroughput

=> 10 MB/s (SCSI/FC HD)(6.6MB/s for (S)ATA disk)

medialab

Storage Performance

3 Key Components:

MediaFile System

( GPFS, AVID-ISIS, Isilon, Omneon Media Grid, XSAN, … )

Hard Disk Network( Disk network, SAN )

100 %Efficiency

medialab 9

FC Network

Fileservers

Clients

Video

Proprietaire oplossingenSchaalbaarheid???

FC-client Based

Avid-Unity / Quantel / SGI-CXFS /

Thomson GV / …

IBM-GPFS / SGI-CXFS /HP-Lustre / ADIC-Stornext /

Nexsan / …

SAN

File servers cluster

Clients Clients Clients

Parallel accessfile system

MXF-AAF video/audio file

IP-client based

Media IP Network

GPFS (IBM)

(General Parallel File System)

Scalability

Reliability

Parallel Throughput

Storage Capacity

Media File System

© VRT-medialab: onderzoek en innovatie10StorageCapacity

StorageThroughput

Classical ITStorage

SD MediaStorageX

Architecture needs to be “tuned” for media production

File-based Media Production


Digital Media Factory Storage Clusters

MCBE

GMI I

Taperobot

BUBE

Cluster3x4CPU

IP netwerk

Cluster3x2CPU

Cluster3x2CPU

Cluster3x2CPU

TSM2x2CPU

TSM1x2CPU

TSM1x2CPU

TSM2x2CPU

SATA mirror40 TBAudio

Other essence

FC mirror62,5 TB

News FeedsNews archive

FC mirror22,5 TBBrowse

SATA single56 TBWork

SANswitch

SANswitch

FC mirrorSATA mirrorSATA single

Taperobot

Taperobot

SANswitch

SANswitch

50 stromen x 50 Mbit/sReal-time

200 stromen x 1 Mbit/sBest-effort

Taperobot

StagingMCRT

Storage Cluster per Service Level


Central storage

INGEST

MediaAsset Mgmt

IP network

CentralStorage

Draft Processing

Concurrentengineering

Browse Clients Processing duringingest/transfer

POSTProduction

Craft editors

Subtitling

Archiving

Analysis

Annotation

Follow-upcontinuity Editor-in-Chief

Post-sonorisation

PLAY-OUT

8 GB P2 card= 32 min videoDV25 (news)

Go Live - 17 June 2007


Online searching, re-viewing and cutting


One-click re-purposing of content


Assertion

• There is a mismatch between business requirements and state-of-the-art technology;• Media technology suppliers don’t sufficiently understand the limitations of IT technology and conventional IT suppliers don’t have sufficient understanding of media production;• Proper research and development are essential...


2009 - Reconciliation of Requirements

Production Supply Chain Automation Real-time co-production

Operational Excellence Simplify system architecture Standards implementation

Scale-out Extend to non-news Prepare for High-Definition

Non-linear distribution Integration of live and on demand Accurate Item-level metadata


Objectives - Back-Office

Production Platform

Master Data Management

Production and distribution

Infrastructure – Networks and Storage

IngestMedia

Asset Mgnt

Editing

Playout

Mastering 2. Scale out Infrastructure Better applications reduce file transfers State-of-the-art technology - Cost reduction

1. Operational Excellence Less applications reduce system integration Metadata standards replace custom development

3. Master Data Management Rationale – Effective(!) re-use Structured Metadata Item-level metadata


Challenge – Operational Excellence

Production Platform




IngestMedia

Asset Mgnt

Editing

Playout

Mastering


System Integration Approach

Assumptions Ad hoc plumbing (“data pump”) is a source of inconsistency; Collaborative production and re-use of material rely on a single central repository.

Supply Chain Engine

Issues None of the available state-of-the-art production applications have been designed to integrate

and neither they have been originally designed for file-based production; Commercially available MAM systems that lack essential functionality;

• While an Enterprise Service Bus is assumed to protect overall consistency and performance, it is sensitive to complexity as well and may become a source of instability on its own.

MAMsystem

The level of Operational Excellence that can be achieved by an Integrated System is determined by the extent to which individual applications have been designed to integrate and by the level of (metadata) standards implementation.


Lessons Learned – Principles of System Integration

Everything should be made as simple as possible, but not one bit simpler.


Challenge – Scale out Infrastructure

Production Platform




IngestMedia

Asset Mgnt

Editing

Playout

Mastering


A single Ingest operation causes 36 file transfers!

GMII

Data TapeArchive

TSM2

TSM1

Data TapeBackup

TSM2

TSM1

IP network

Agility

Polopoly

Output

OT

OT

Nieuws Playout

Omneon

MER

Omneon

Ardome

MAM

ENCENCENCENCENCENCENCENCENCENC

Encoders

ENCENCENCENCENCENCENCENCENCENC

MCRT MCRT MCRT TSM

//news

//feeds /cut

/import

MCRT cluster

MCBE MCBE MCBE TSM

//lowres

//audio

//sec

MCBE cluster

BUBE BUBE BUBE TSM

/wip

/archive

/sec

BUBE cluster

IP network

Opslag//news//feeds Precut

/import

Ardex

Opslag/wip

/archive

Opslag//lowres//audio

Opslag/sec

Opslag//sec

DV25

D10

OPATOPAT

OPATOPATOPAT

OPATOPATOPATOPATOPAT

OP1A

OP1AOP1A

OP1A

OP1A

OP1A

OP1AOP1A

Lres

OP1A

Lres

OP1A OP1A

OPAT

OPAT

OP1A

OP1A

OPATOPATOPATOPAT

OPATOPATOPATOPAT

Browse Clients

Lres

Workflow vs Dataflow:

00010203040506070809101112131415161718192021222324252627282930313233343536

High Definition Media Production Infrastructure


High Definition Media Production InfrastructureHigh Definition Media Production Infrastructure

Agility

Polopoly

Output

OT

OT

Nieuws Playout

Omneon

MER

Omneon

Introduction: Issues – HD Digital Media Factory

HD Media Infrastructure Issues• Complex Workflow -> Inefficient Dataflows

• More media-formats: Transcoding becomes bottle-neck!

• Increased Storage Capacity Requirements: HD ~= SD x 2 -> HD Storage Architecture???

Backup/restore windows HD ~= SD x 2Reconsider # copies – storage protection

• Cost: Target HD ~= SD / 4 (not counting disk price erosion)

Geisha results IBC 2009


HD Storage Architecture Requirements

StorageCapacity

StorageThroughput

HD MediaStorageX

Classical ITStorage

SD MediaStorageX

“Tuned” Fibre Channel Architecture

Geisha results IBC 2009


High Definition Media Infrastructure

State-of-the-Art • Currently 160TB storage capacity (3200 hrs HD) per cluster• Demonstrated troughput 40Gbps• Built-in Disaster Recovery• CAPEX - 2.2€ per GB


Objective 3 – Effective re-use

Production Platform




Media Asset Management Issues• Production Environment

• No upfront selection causes information overload;• Multiple sources of more or less structured metadata;• Parallel sources cause dupplicates;• International context - multilingual


Objective 3 – Effective re-use

Production Platform




Media Asset Management Issues• Production Environment

• No upfront selection causes information overload;• Multiple sources of more or less structured metadata;• Parallel sources cause dupplicates;• International context - multilingual

• Professional Requirements• An item is relevant or it is not;• Retrieve all relevant items;• No false positives

• Opportunities• Recognise workflow and trace dependancies;• Integrate with Product Engineering (scripts,...);• Computer Vision techniques may help.


The state of the Art…

archiefnummer : ALG 20010813 1fragmentnummer : 1 reeks : 1000 ZONNEN EN GARNALENbandnummer : E03024404formaat : DBCMfragmenttitel : 1000 ZONNEN & GARNALENbeeld : KL/PALPLUSfragmentduur : 18 20 tekst : 0'00" TOERISTISCH REPORTAGEMAGAZINE OVERZICHT ONDERWERPEN GENERIEK TOERISTISCH REPORTAGEMAGAZINE, OVERZICHT ONDERWERPEN 0'50" VANDAAG : KUNSTENAAR LUC HOFKENS ONTWIERP EEN OASE OP ZIJN DAKTERRAS IN BORGERHOUT DIE DOET DENKEN AAN DE GRAND CANYON INTERVIEW MET LUC EN ZIJN VROUW MARILOU BUITENBEELD DAK MET OMGEVING BUITENKANT ARBEIDERSWONING, PANO OVER ROTSWANDEN, KRATEN MET WATER, BEPANTING, FOTOALBUM MET VERLOOP WERKEN 4'00" JUNIOR : KLAARTJE ALAERTS, 13 JAAR WIL ASTRONAUTEN WORDEN ZE BEZOEKT HETEUROSPACE CENTER METRUIMTEVEREN, RAKETTEN SIMULATIE IN RUIMTEVEER, INTERVIEW, HEEFT EEN UFO GEZIEN MAAKT ZELF KLEIN RAKETJE, SCHIET HET AF 7'50" DE SCHEURKALENDER : ARCHIEF RECLAMEFILM IBM INTERVIEW MAURICE DE WILDE, EERSTE PERSOONLIJKECOMPUTER trefwoorden : BELGIE; BORGERHOUT; ARTIEST; OASE; KUNST; GRAND CANYON (NATUURGEBIED); DAK; TERRAS; INTERVIEW; EURO SPACE CENTER; RUIMTEVAART; PC; BOOTTOCHT; RIJKDOM; PASSAGIER; GASTRONOMIE; RESTAURANT; PERSONEEL; VAKANTIE; BINNENBEELD; SCHIP; BECKERS LEEN; VRT; LOTTO; RADIOOMROEPSTER; KLANKSTUDIO; UITVINDING; BARBECUE; BETONMOLEN; IBM; RECLAMESPOTrechthebbende : VRT

Opzoekscherm FILM Set: 16 Aantal: 1blz 1 van 3 trefwoorden: ibm and vrt archiefnummer: - uitzendjaar: maand: dag: fragmentnummer: fragmentduur: reeks: formaat: bandnummer: aflevering: afleveringsnummer: programma: uitzenddatum: fragmenttitel: tekst: kategorie: opnamedatum: opnamenummer: journalist: rechthebbende:

SETS The strings required for the operation are not defined

F11 F12 F13 F14 F17 F18 F19 F20 Ent Eindigen Sets Refset Toon Vorige Volg/Leeg Thesaurus Commando Opzoeken


Shot Segmentation and Scene Recognition


Face recognition and identification


Media Asset Management - Opportunities

An integrated data-model and a consistent application framework are the key to production

automation and supply chain integration

!

(≠ Digitizing analogue and disintegrated workflows)

• Professional context - trace dependencies

• Scripts contain essential master data – integration with creative processes is a must

• Computer Vision does not replace formal annotation, but substantially accelerates the process and increases the accuracy


Conclusion

Production Platform




IngestMedia

Asset Mgnt

Editing

Playout

Mastering

2. Scale out Infrastructure Avoid file transfers Cheaper systems for cost reduction

1. Operational Excellence One set of numbers – reduce the number of apps Less apps reduce the need for system integration Replace custom development by proper standards

3. Master Data Management Trace dependancies Integrate with creative processes Explore and integrate automatic analysis tools



Broadband distribution

Production Platform




D a t a G e n e r a l






MetaData

MetaData

Communication(Information)


Data

Distribution via DVB, IP or other

Video Audio


2004 – Live Streaming


Xmas 2007 – BBC iPlayer


Broadcast...

A customer can have a car painted any colour that he wants so long as it is black.

(Henry Ford)


Good, better,...

Anywhere• “Live streaming”• Radioplayer software application• Quick win – “in series” live transcoding

Anytime• “Podcast”, “iPlayer”• On demand complements live offering• Major impact – metadata power-lifting

The Ultimate Radio Exp - myRadio• Mobile - Anywhere, anytime and impulse-driven• Seamless integration of live and on demand• Cross-platform continuity?


The Ultimate Radio Exp(eriment)

myRadio @time of writing• Excellent quality where possible, lower bit-rate if necessary• Non-linear distribution assumes broadband distribution• Open distribution framework• Http adaptive streaming enables bandwidth-friendly distribution

http://myradio.vrt.com

http://myradio.vrt.com/


Ongoing – Non-linear distribution

Seamless integration of Live and On demand• “Bookmark” by tagging• Pause, Back and Fast Forward • Radio search engine• Assemble myRadio

TO DO – Track each individual item• Audio segmentation • Speech recognition and indexing• Metadata harvesting (!!!)


Business Case for Radio

IP Distribution• +/- 5.000.000 hours per dag• à 128 kbps, 250 TB, 90PB per year• à 70.000€ per PB• Total – 6.300.000€

Vgl. Homepage (deredactie.be)• 2MB• 128 seconden high-def audio(!)


‘Supply Chain Integration’


Data

InformationCommunication

Distribution to TV, PC and mobile via DVB and IP

Video Audio

InformationMgnt

Infrastructure(network and storage)


Media Asset Mgnt


Trend – Production System Virtualisation


Trend - From « metadata » to CAD/CAM

?


Proof of Concept - Scoop

http://www.youtube.com(search for “scoop”, “ibbt”, “CAD”)

http://www.youtube.com/


Change...

© VRT-medialab: onderzoek en innovatie47 47

http://medialab.vrt.be

http://myradio.vrt.be

[email protected]

http://medialab.vrt.be/

http://medialab.vrt.be/

Date post:	16-Feb-2017
Category:	Technology
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