This document summarizes Juan Pedro López Velasco's thesis work on developing visual attention and perception models for assessing video quality. The work has two main objectives: 1) Predicting visual discomfort in 3D stereoscopic video by analyzing factors like motion, disparity, and parallax changes. 2) Improving 2D video quality metrics by applying visual attention models that weight regions of interest to better correspond to human perception. The work involves conducting subjective testing to determine important quality factors, developing computational models of visual attention, and incorporating these models into new objective metrics to provide more accurate quality assessment.

1. Visual attention and perception
models for assessing quality in 2D
and 3D stereoscopic video
Juan Pedro López Velasco - jlv@gatv.ssr.upm.es
Advisor: José Manuel Menéndez García - jmm@gatv.ssr.upm.es
Universidad Politécnica de Madrid
Madrid, 8th February 2016

2. 2
Index
• Introduction
• Objectives and Work Development
• Visual discomfort prediction in 3D
stereoscopic video
• Visual Attention Model for Video Quality
Assessment
• Conclusions and Future work
• Merits

3. 3
Introduction
• Quality of Experience (QoE) is defined as the degree
of delight or annoyance of the user of an application
or service, in this case, multimedia services.
• Necessary: Estimation of QoE in different stages of
video broadcasting dataflow and for a variety of
sources: 2D and 3D.

4. 4
Scenarios
CONTENT
CREATION
PHASE:
Visual comfort
assessment
(3D)
COMPRESSION
PHASE:
Visual attention
and saliency
models (2D)

5. 5
…the final user.
The most important thing in video quality
assessment is…

6. 6
ObjectivesandWork
Deevlopment

7. 7
Objectives (I)
For visual comfort assessment (3D):
• Detecting empirically the main sources of visual discomfort in 3D
stereoscopic video after developing subjective assessment.
• Quantifying the situations of sequences where the probability of visual
discomfort to occur is higher.
• Analyzing the factors of motion, distribution of parallax and disparity
change in pairs of sequences for developing tools that correspond to
human perception.
• Demonstrate with sequences that the results obtained in subjective
assessment may be predicted with objective parameters and
characteristics measurement.

8. 8
Preliminary
subjective
assessmnet
Determination of
visual discomfort
sources
Characterization
of video
sequences
Statistics
analysis, new
subjective
assessment,
metrics
development
and drawing
conclusions
Work Development (I)

9. 9
Objectives (II)
For visual attention and saliency models (2D):
• Improving objective quality metrics by applying visual attention models,
which weight regions of interest to obtain results closer to human eye’s
response.
• Determining accurate visual attention models, particular for each sequence,
which predict the most probable areas observed by the user.
• Weighting the saliency factors analyzed by the use of subjective
assessment. These saliency factors are the following: motion, level of
detail, face detection and position of pixel.
• Demonstrating the improvement of the objective metrics for measuring
quality and artifacts in the sequence when applying the developed visual
attention model (Advanced Blur metric)

10. 10
Determining
factors:
motion, face
detection,
level of
detail and
position
Subjective
assessment
with artificially
impaired
sequences
Weighting these
factors in order
of importance.
Visual attention
model
generation
Application of
model in
objective
metrics
(Advanced Blur
metric)
Work Development (II)

11. 11
Visualdiscomfortpredictionin
3Dstereoscopicvideo

12. 12
Introduction to Stereoscopy
• Stereoscopic 3D video perception is based on the fact that two
different video signals (different but highly correlated) are
captured in order to feed each of the viewer’s eyes.
• One signal is received by the left eye and another one by the
right eye. The brain fuses left and right view.
• 3D video imitates the binocular human vision (natural view).
• The cyclopean eye is an imaginary eye situated midway
between the two eyes.

13. 13
Disparity and Parallax
• Disparities are the differences between the angles subtended
between pairs of features.
• Parallax is created by disparities: Positive, negative or zero,
depending on the position of the object respect to the screen.

14. 14
Example of 3D disparity

15. 15
Accommodation-Vergence conflict
• Viewing an object in stereoscopic displays:
– Eyes accommodate to the screen
– But when rotating to fix the apparent object (vergence)
– an inconsistency between them occurs (derived from stereopsis).
• This effect is the accommodation-vergence conflict.

16. 16
Problem description
• Disparity may offer an incredible experience, BUT differences in 3D
disparity eye may have difficulties to focus objects causing visual
discomfort, annoyance, headache.
• The eye focus the objects: Accommodation of the eyes needs
enough time to adapt to changes for correct vision of 3D videos
(importance of motion).
• Common sources of visual discomfort:
– Excessive binocular parallax (especially negative)
– Accommodation and vergence mismatches (AVM)

17. 17
Accomodation-Vergence Mismatches
(AVM)
• AVM is one of the most frequent sources of visual discomfort in
3DTV.
• When position of the objects change (parallax), the
accommodation is constant but the vergence changes.
• The crystalline must adapt to change fastly.
Near distance object Far distance object

18. 18
Zone of Comfort
• Zone of Comfort (ZoC) is a term introduced by Percival (1892) to
define the relationship between distance of vergence and distance to
the screen (accommodation distance).
• Studies focused on static images (Shibata, 2011)

19. 19
Work methodology
Characterization of individual
video sequences
Sequence
Motion
Depth map
Distribution
of parallax
1
Sequence
1
Sequence
2
Combination of video pair
sequences
2
Wide casuistic of transitions
Subjective assessment with
pairs of sequences for
transition analysis
3 Analysis of when visual
discomfort happens4

20. 20
Characterization of video sequences
• Tools for characterization:
– Depth maps: using SAD (Sum of Absolute Differences) techniques.
– Histograms of parallax information (based on depth map information)
– Diagrams of TI (Temporal Information) and SI (Spatial Information) variation.
SAD

21. 21
Case of study: Sequence Palco HD
• Separation of virtual cameras over the average interpupillary
distance. Human eye adapts to change produce by negative
parallax, but… abrupt transition generates discomfort.
Progressive
Temporal Parallax
variation

22. 22
Subjective Assessment
• Analysis of changes / transitions between pairs of video
sequences to determine a preliminary ZoC.
• Analysis of transitions between scenes:
– Selection of sequences with different values of SI (Spatial Information) and TI
(Temporal information), bidimensional information.
– Selection of sequences with diferent values of spatial and temporal parallax
variance (negative, parallax), tridimensional information
• Test conditions (following Recommendations BT.500 and P.910)
– 74 observers
– 65 inches television
– Observation distance: 2,5 m
– HD sequences
– Annoyance 5-notes Scale
MOS
Scale
Annoyance derived from
transition
Quality of Experience
5 Very comfortable Excellent Experience
4 Comfortable Good Experience
3 Mildly uncomfortable No visual discomfort
2 Uncomfortable Visual discomfort
1 Extremely uncomfortable High visual discomfort

23. 23
Results of subjective assessment

24. 24
Transition: Angel to Ladder (I)
 40% of the people gave a score that manifests visual discomfort

25. 25
Transition: Angel to Ladder (II)
Parallax variation in pixel

26. 26
Transition: “Spaceship” to “Astronaut”
 Negative parallax in right side of first video to negative/positive
combination

27. 27
Transition: “Station” to “Itaca3d”
 This is the worst scored transition in the tests
↑↑Motion
↑↑Motion
Hiperstereoscopy!

28. 28
Transition: “Boxers” to “Dance”
 Negative parallax located in different areas, less annoyance for
observers.

29. 29
Transition: “Hall” to “Laboratory”
 Both videos with negative parallax in both videos and window
violation → low scores.
Window violation!

30. 30
Conclusions
• After subjective assessment, results indicate the necessity of
evaluating both static disparity and dynamic variation of the
stereoscopic image, in terms of motion.
• ZoC is affected by motion in the scene. The state-of-the-art must be
actualized to offer results with tests of dynamic sequences.
• Avoiding visual discomfort is possible locating objects in positive
parallax, BUT that implies a consequent decrease of QoE.
• Negative parallax must be controlled to generate soft variations:
– Fast variation of negative parallax is usually the main source of visual discomfort,
especially when the transition is produced to a content with a completely different
disparity diagram.
– Only hyperstereoscopy (i.e. pixels with negative parallax with disparities higher than 5)
in the sequence is not enough for detecting visual discomfort, it is the transition what
provokes the discomfort.
• Positive parallax is recommended for its tolerance to visual discomfort
and the consequent.

31. 31
Future work
After the conclusions obtained after detecting the main sources of
visual discomfort:
• Developing recommendations and guidelines for 3D
contents creators.
• Generating tools for automatic detection of discomfort in 3D
videos.

32. 32
VisualAttentionModelforVideo
QualityAssessment

33. 33
Contents
• Introduction: Problem description
• Calibration of the visual attention model
– Artificially impaired video sequences generation:
 Analysis of video characteristics by regions
 Creation of masks based on ROI’s
• Results and examples with test sequences
• Advanced blur metric
– Application to real video sequences (encoded in H.264 at different bitrates)
• Conclusions

34. 34
Problem description (I)
• Assessing video quality is still a complex task.
• Video Quality Assessment needs to correspond to human
perception.
• Visual attention is focused on concrete regions (ROI’s) of an image
as demonstrated with fixation maps and eye-tracking.
Original image Fixation map Image with visual
attention weights

35. 35
• Most pixel-based metrics do not present enough correlation
between objective and subjective results
• Algorithms need to correspond to human perception when
analyzing quality in a video sequence.
• For example, these four frames have the same MSE.
• Video quality metrics should correlate with visual attention and
psychovisual models adapted to concrete artifacts and their
visualization.
Problem description (II)
High blocking High blurring (defocus) Salt and pepper noise JPEG encoding

36. 36
Visual Attention Features
• According to context-aware saliency detection model proposed by
Goferman et al [GOFERMAN-1, 2012], image regions of interest are
detected based on four principles of human attention supported by
psychological evidence
– Low-level characteristics affecting to each individual pixel, such as color
and contrast
– Global considerations, which suppress frequently occurring features,
while maintaining features that deviate from the norm.
– Visual organization rules which state that visual forms may possess one
or several centers of gravity about which the form is organized
– High-level factors, such as human faces or concrete objects recognition.
This factor could be content dependent, but human faces generate specific
patterns in human retina that increase the probability of be perceived
related to psychological and cognitive features.

37. 37
Example of artificially impaired
sequences
• Impaired area (with blocking artifact) located in human faces
ROI.
• This effect is excessive in this example but in real life is a
common effect.

38. 38
Work methodology
• Objectives:
– Calibration of the influence of features (ROI) for determining the
visual attention model.
– Creation of Advanced Blur Metrics
• Methodology for Visual Attention Model:
– Selection of ROI’s: motion, faces, spatial detail and position.
– Creation of masks for artificially impaired sequences (adapted to
concrete artifact: blurring).
– Subjective Assessment: Opinions of users (MOS scaled).
– Search for inconsistencies between subjective assessment (MOS
obtained) with pixel-based objective metrics (PSNR), to weight the
influence of each feature.
• Advanced Blur metric: loss of energy (blur) adapted to visual attention.
• Tests: Once the visual attention model is generated, it will be tested
with real sequences (distorted by the effect of H.264 encoding).

39. 39
Scheme of artificially impaired video
sequences generation
Impaired
video
sequence
Original
video
sequence
Artificially
impaired
sequence
Inverse
Feature
Mask
Feature
Mask
Distortion
(2 sequences
for each distortion:
One and the
opposite case
As seen in next
example)

40. 40
Impairment and artifacts insertion process
Original
video
sequence
Artifact
Distortion
Impaired
video
sequence
Blocking
Blurring
Ringing
Blocking simulated with 8x8
mosaic filter
Blurring simulated with
gaussian lowpass filter
Ringing simulated with JPEG
codification filter

41. 41
Creation of masks based on ROI’s (I)
• Types of regions of interest for masks
Original
video
sequence
Feature
Detection
Feature Mask
Inverse
Feature Mask
Motion
Spatial
Detail
Faces
Position
Color

42. 42
Motion mask
• For motion detection, temporal information in consecutive
frames is scrutinized
• Temporal information is analyzed:
  0),(),(,.),( 1   yxFyxFifMaskyxPix frameiii
Original frame Motion mask based on TI

43. 43
Spatial Detail Mask
• Textures, edges and objects in motion are the source of hiding or
highlighting a determined impairments, in cases such as blocking
or blurring artifacts.
• Canny algorithm is used to create binary masks for separating
homogenous from high-frequencies areas.
Original frame Spatial detail mask based on Canny algorithm

44. 44
Pixel Position Masks
• The image is divided in 9 sections (Nojiri, 2009)
• Objective: Analyzing influence of pixel position by areas.
• Three types of masks are created depending on the regions:
Nojiri’s sections
distribution
Corner mask Lateral mask Central mask

45. 45
Facial Mask
• Haar algorithm included in OpenCV libraries based on a
boosted cascade of simple features is used for face detection
Face detection Face mask

46. 46
Subjective assessment for calibration
• Results based on subjective tests are analyzed to demonstrate
the validity of test sequences. Spatial detail is analyzed in these
3 sequences.
• MOS scale is used: 5 (excellent) to 1 (Poor)
“News Report”: Faces “Barrier”: Motion “Crowd”: Pixel Position
Sequence
FR
Metric
H.264
Impairment located in
Faces ROI.
75Mbps 500Kbps D. Inv.
News
Report
PSNR 47.93 37.58 46.82 34.52
Blur 0.44 3.63 0.38 5.17
MSE 0.67 1.93 0.10 2.30
MOS 4.81 1.54 1.33 3.78
Sequence
FR
Metric
H.264
Impairment located in
Motion ROI.
75Mbps 500Kbps D. Inv.
Barrier
PSNR 49.82 33.19 39.85 34.24
Blur 0.27 8.36 1.97 6.24
MSE 0.51 3.34 0.359 2.98
MOS 4.77 1.33 3.11 3.89
Seq.
FR
Metric
H.264 Impairment located in Position ROI’s
75
Mbps
500
Kbps
Center Lateral Corner
D. Inv. D. Inv. D. Inv.
Crowd
PSNR 34.33 25.34 30.74 26.82 33.87 26.00 35.95 25.88
Blur 3.44 22.55 6.27 15.33 2.60 19.44 0.95 22.47
MSE 3.55 8.76 2.30 6.21 1.21 7.30 0.64 7.87
MOS 4.68 1.22 1.44 2.44 3.78 1.33 4.11 1.22

47. 47
Calibration of Faces
• Distortion is located in the human faces ROI
• Subjective MOS values are lower (1.33) than when located in
the rest of the picture and faces appear sharp (3.78)
• Inconsistence with objective metrics: PSNR (46.82 vs. 34.52) or
MSE’s behavior (0.10 vs. 2.30)
Sequence
FR
Metric
H.264
Impairment located in
Faces ROI.
75Mbps 500Kbps D. Inv.
News
Report
PSNR 47.93 37.58 46.82 34.52
Blur 0.44 3.63 0.38 5.17
MSE 0.67 1.93 0.10 2.30
MOS 4.81 1.54 1.33 3.78

48. 48
Calibration of Motion and Faces
• A similar situation occurs when analyzing motion in “Barrier”
sequence. Inconsistence with objective metrics.
• Inconsistencies in corner regions between MOS and objective
metrics, such as PSNR, for sequence “Crowd”.
• Inconsistencies in spatial detail areas, less
Sequence
FR
Metric
H.264
Impairment located in
Motion ROI.
75Mbps 500Kbps D. Inv.
Barrier
PSNR 49.82 33.19 39.85 34.24
Blur 0.27 8.36 1.97 6.24
MSE 0.51 3.34 0.359 2.98
MOS 4.77 1.33 3.11 3.89
Seq.
FR
Metric
H.264 Impairment located in Position ROI’s
75
Mbps
500
Kbps
Center Lateral Corner
D. Inv. D. Inv. D. Inv.
Crowd
PSNR 34.33 25.34 30.74 26.82 33.87 26.00 35.95 25.88
Blur 3.44 22.55 6.27 15.33 2.60 19.44 0.95 22.47
MSE 3.55 8.76 2.30 6.21 1.21 7.30 0.64 7.87
MOS 4.68 1.22 1.44 2.44 3.78 1.33 4.11 1.22

49. 49
Relative influence of factors
• After subjective assessment we concluded that the following
chain of influence has been considered
Faces > Central > Motion > Detail > Lateral > Corner

50. 50
Example of psychovisual model defined (I)
Frame from sequence “News Report”

51. 51
Example of psychovisual model defined
(II)
Motion Mask Spatial Details Mask
Pixel Position Mask Faces Mask

52. 52
Advanced Blur metric
• Blur metrics calculates the loss of energy when compressing a
video sequence with transforms, such as DCT. Blur is the
comparison of gradient between reference and distorted image
• Advanced Blur includes the effect of visual attention model.






1
0
1
0
)),(()),((),(
W
j
H
i
codref jifGEjifGEjipsyBlur








1
0
1
0
)),(()),((
1 W
j
H
i
codref jifGEjifGE
HW
Blur
Advanced Blur:



 3
0
)(
),(),(),(),(
),(
c
MAX
FACESPOSDETMOT
ccoefHW
jicoefjicoefjicoefjicoef
jipsy

53. 53
Test with real sequences
• Real sequences encoded at different bitrates:
– H.264: 6Mbps – 500Kbps (HD Sequences)
Umbrella Boxers
Tree BranchesPhone Call

54. 54
Results (I)
• Results of sequences compared to MOS (subjective opinión),
PCC (Pearson Correlation Coefficient), and the improvement
from conventional Blur metric to Advanced Blur metric.
Sequence Value 6Mbps 4Mbps 1Mbps 500Kbps PCC
Δ(Adv.Blur-
Blur)
Boxers
Blur 0,650 0,920 3,040 6,880 -0,953
2,97%
Adv Blur 1,340 1,480 2,000 2,660 -0,983
MOS 4,778 4,111 2,444 1,333
Hall
Blur 0,790 3,280 14,180 27,230 -0,982
1,40%
Adv Blur 2,440 3,490 6,880 9,670 -0,996
MOS 4,889 4,111 2,667 1,556
Phone Call
Blur 1,950 2,260 3,460 4,490 -0,990
0,94%
Adv Blur 1,640 1,780 1,990 2,170 -0,999
MOS 4,889 4,000 2,444 1,333
Tree Branches
Blur 11,920 17,360 22,380 20,120 -0,863
13,24%
Adv Blur 6,150 8,030 9,790 12,090 -0,996
MOS 4,889 3,778 2,556 1,550

55. 55
Results (II)

56. 56
Conclusions
• Algorithms are not adapted to subjective human eye response.
• Subjective tests revealed the importance of some concrete
regions.
• Visual attention models adapted to visual attention obtain better
correlations when weighting regions of interest (ROI) and adapted
to concrete artifacts.
• The use of visual attention models obtains improvement in
objective metrics (Advanced Blur metric) up to 13% compared to
conventional methods.

57. 57
ConclusionsandFutureWork

58. 58
Conclusions
• ZoC is affected by motion in the scene. The state-of-the-art
must be actualized to offer results with tests of dynamic
sequences. Motion is a key factor in visual discomfort.
• Avoiding visual discomfort is possible locating objects in
positive parallax, BUT that implies a decrease of QoE:
– Negative parallax must be controlled to generate soft variations.
– Positive parallax is recommended for its tolerance to visual discomfort and
the consequent.
• Subjective tests revealed the importance of concrete ROI’s.
• Visual attention models adapted to visual attention obtain better
correlations when weighting regions of interest (ROI) and
adapted to concrete artifacts.
• The use of visual attention models obtains improvement in
objective metrics (Advanced Blur metric) up to 13% compared
to conventional methods.

59. 59
Future work
• Development and patent of a system for automatization of
quality of Experience for content generation (measuring visual
discomfort).
• Developing recommendations and guidelines for 3D contents
creators.
• Improvement of Visual attention model with more low, medium
and high level features, such as color.
• Advanced metrics adapted to other artifacts, such as blocking.
• Development of No-Reference metrics including visual attention
models.

60. 60
Merits

61. 61
Publications (I)
Peer-reviewed international journal articles (1)
• López, J. P., Rodrigo, J. A., Jiménez, D., & Menéndez, J. M. (2013). Stereoscopic 3D video
quality assessment based on depth maps and video motion. EURASIP Journal on Image and
Video Processing, 2013(1), 1-14. December 2013. Impact Factor: 0.74. JCR Indexed.
Peer-reviewed international conference papers (9)
• López, J. P., Rodrigo, J. A., Jimenez, D., & Menendez, J. M. Subjective quality assessment in
stereoscopic video based on analyzing parallax and disparity. Consumer Electronics (ICCE),
2015 IEEE International Conference on. Las Vegas (U.S.A.), January 2015.
• López, J. P., Rodrigo, J. A., Jimenez, D., & Menendez, J. M. Proposal for characterization of
3DTV video sequences describing parallax information. In Consumer Electronics (ICCE), 2015
IEEE International Conference on. Las Vegas (U.S.A.), January 2015.
• López, J. P., Slanina, M., Arnaiz, L., & Menéndez, J. M. Subjective quality assessment in
scalable video for measuring impact over device adaptation. In EUROCON, 2013 IEEE (pp.
162-169). Zagreb (Croatia), July 2013.
• López, J. P., Rodrigo, J. A., Jimenez, D., & Menendez, J. M. Insertion of Impairments in Test
Video Sequences for Quality Assessment Based on Psychovisual Characteristics. Artificial
Intelligence, Modelling and Simulation, International Conference on. Madrid, November 2014.
• López, J. P., Rodrigo, J. A., Jimenez, D., & Menendez, J. M. Definition of masks related to
psychovisual features for Video Quality Assessment. In Consumer Electronics (ISCE), 2015
IEEE International Symposium on (pp. 1-2). Madrid, June 2015.

62. 62
Publications (II)
• López, J. P., Jimenez, D., Cerezo, A., & Menéndez, J. M. No-reference algorithms for video
quality assessment based on artifact evaluation in MPEG-2 and H. 264 encoding standards.
IFIP/IEEE International Symposium on. IEEE. Ganthe (Belgium), May 2013.
• Rodrigo, J. A., López, J. P., Jiménez Bermejo, D., & Menendez Garcia, J. M. (2013). Automatic
3DTV Quality Assessment Based On Depth Perception Analysis. Nem Summit 2013
Proceedings, 69-74. Nantes (France), October 2013.
• López, J.P., Jiménez, D., Díaz, M., & Menéndez, J.M. Metrics for the objective quality
assessment in high definition digital video. IASTED International Conference on Signal
Processing, Pattern Recognition and Applications (SPPRA). 2008.
• López, J.P., Díaz, M., Jiménez, D., & Menéndez, J. M. Tiling effect in quality assessment in high
definition digital television. 12th IEEE International Symposium on Consumer Electronics-
ISCE2008, ISBN: 978-1-4244-2422-1, Vilamoura, April 2008.
Book chapters (1)
• López, J.P. Video Quality Assessment. Video Compression, Ed. InTech, ISBN: 978-953-51-
0422-3, March 2012.
Other peer-reviewed international conference papers (5)
Peer-reviewed national journal articles (1)

63. 63
Research projects
• ACTIVA. Ministerio de Industria, Turismo y Comercio (FIT-330300-2007-42).
• BUSCAMEDIA: hacia una adaptación semántica de medios digitales multirred-multiterminal. [2009-2012].
• CIUDAD2020: Hacia un nuevo modelo de ciudad inteligente sostenible. [2011-2014].
• COST Action IC1105: 3D-ConTourNet 3D Content Creation, Coding and Transmission over Future Media Networks.
• EPSIS. Entretenimiento y publicidad segmentada en entornos inmersivos. Ministerio Economía y Competitividad [2011-
2013].
• FURIA 2009. Futura red integrada audiovisual. Ministerio de Industria, Turismo y Comercio (TSI-020301-2009-33) [2009-10]
• HBB4ALL Hybrid Broadcast Broadband TV For All. [2013-2016]
• HORFI-Radar MIMO de banda ultra ancha. TEC2012-38402-C04-01 HORFI.
• ICT 2020. Ministerio de Industria, Turismo y Comercio (TSI-020302-2011-23). [2011-2013]
• IMMERSIVE TV: Una aproximación a los medios inmersivos. Ministerio de Industria, Turismo y Comercio [2010-2012].
• ITACA 3D. Plataforma de creación, producción y distribución de video estereoscópico de entretenimiento para la
visualización de televisión en 3D a través de briadcast. Ministerio de Industria, Turismo y Comercio (TSI-020110-2009-396).
• MELISMAS - Generación automática de mensajes en lengua de signos para aplicaciones sanitarias. Ministerio de
Economía y Competitividad (RTC-2014-2762-1). [2014-16]
• Palco HD. Convergencia de plataformas digitales hacia la HD y medidas de calidad asociadas. Ministerio de Industria,
Turismo y Comercio. [2007-2009]
• PALCO HD2. Ministerio de Industria, Turismo y Comercio. [2009-2011].
• PLEASE Plataforma de alta eficiencia avanzada para distribución de contenidos [2014-15].
• PRO-TVD-CM PRO-TVD-CM: Proyecto Integral de Investigación en Televisión Digital (S0505/TIC-0398). [2005-2009]
• S3D: Equipo servidor-editor de vídeo 3D realizado en colaboración con las empresas Overon y Aicox.
• SIRENA: SIstemas y tecnologías 3D Media sobre Internet del Futuro y REdes de difusión de NuevA generación. Ministerio
de Economía y Competitividad (IPT-2011-1269-430000). [2011-2013]

64. 64
Thanks for your attention!!
For more information:
jlv@gatv.ssr.upm.es