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این دو عالم علم دارد در نهاد منتخب وان جهانی رمز دارد در حروف مختصر سنایی غزنوی. Image/Video Compression & VOD. برنا فيروزي نويد زرين درفش محمود قديمي مهندسي فناوري اطلاعات بهار 88. Image Compression. Compression. - PowerPoint PPT Presentation
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منتخب نهاد در دارد علم عالم دو اینمختصر حروف در دارد رمز جهانی وان
غزنوی سنایی
Image/Video Compression & VOD
فيروزي برنادرفش زرين نويد
قديمي محمود
فناوري مهندسياطالعات
88بهار
Image Compression
3
Compression
( سازی که( Compressionفشرده است پردازشی ،عالیم به را ها داده اضافی، اطالعات حذف با
. دهد می کاهش دیجیتالی برای نیاز مورد باند پهنای به بسته پردازش این
ها داده سازی، ذخیره فضای میزان و ها داده انتقال . دهد می کاهش را
های داده انتقال امکان نیاز مورد باند پهنای کاهش . دهد می واحد زمان یک در را بیشتری
( افزونگی (redundancyكاهش4
،آنالوگ ديجيتال
شود مي انجام ديجيتال فضاي در تصاوير پردازش
قبل بايد باشيم داشته آنالوگ تصويري منبع يك ما اگربه را آن به ابتدا سازي فشرده عمل هر انجام از
كنيم تبديل ديجيتال
5 5
Purpose of Image Compression
Saving storage space Saving transfer time Easy processing Easy to transmitted over network reduce cost
6
هدف
خواستار ما آل ايده حالت در تصوير كيفيت حداكثر منابع پردازش و سازي ذخيره فضاي حداقل
شرايط بهترين در را هدف دو هر توانيم نمي ماباشيم داشته
است؟ چگونه سازي فشرده بهترين
7
Why we want to compress?
To transmit an RGB 512X512, 24 bit image via modem 28.2 kbaud(kilobits/second)
min) 4( second213)second/10248.28(
)/24)(512512(
bits
pixelbitspixels
8
Image Compression
lossless compression
Huffman Coding
Run-length encoding
lossy compression
Predictive Coding Transform Coding
Image Compression Coding
9
سازي فشرده اصلي كالس دوتصاوير
Lossless( اتالف ) بدون تصوير ديتاهاي روي از
دقيقا توان مي شده ذخيرهاصلي به رسيد تصوير
از بدون شده ذخیره تصویرداده كمترين دادن دست
. است تصویر خود ای،Compression rate:
2:1 (at most 3:1)
) Lossy (پراتالف تصوير ديتاهاي روي از
به توان مي شده ذخيرهتصوير نزديكتصويري به
رسيد اصلي خود شده ذخیره تصویر
بلکه نیست، اصلی تصویراطالعاتی و است آن شبیه
است داده دست از را
Compression rate: high compression 10
General compression system model
Input image Output image
Encoder
Source encoder
Channel encoder Channel decoder
Source decoder
Channel
Decoder
11
Compression System Model
Compression
InputInput PreprocessingPreprocessing EncodingEncoding CompressedFile
CompressedFile
OutputOutputPostprocessingPostprocessingDecodingDecodingCompressedFile
CompressedFile
• Decompression
12
Compression Ratio
Ex Image 256X256 pixels, 256 level grayscale can be compressed file size 6554 byte.
Original Image Size = 256X256(pixels) X 1(byte/pixel)
= 65536 bytes
RCSizeFileCompressed
SizeFileedUncompressRationCompressio
106554
65536Rationcompressio
13
Bits per Pixel
Ex Image 256X256 pixels, 256 level grayscale can be compressed file size 6554 byte.
Original Image Size = 256X256(pixels) X 1(byte/pixel)
= 65536 bytesCompressed file = 6554(bytes)X8(bits/pixel)
= 52432 bits
PixelsofNumber
BitsofNumberPixelperBits (2)
8.065536
52432PixelperBits14
Key of compression
Reducing Data but Retaining Information
Various amounts of data can be used to represent the same amount of information. It’s “Data redundancy”
RD C
R1
1
Relative data redundancy
15
Entropy
Average information in an image.
1,,1,0, Lkwheren
np k
k
1
0
L
kkka plL
1
02 )(log
L
kkk ppEntropy
• Average number of bits per pixel
16
Compression Standard
Standard:
Image Video
ISO JPEGJPEG2000
MPEG1,MPEG2, MPEG4, MPEG7
ITU N/A H.261, H.263, H.263+, H.26L
17
Image Compression
lossless compression
Huffman Coding
Run-length encoding
lossy compression
Predictive Coding Transform Coding
Image Compression Coding
18
Loseless Compression
No data are lost Can recreated exactly original image Often the achievable compression is mush less
19
Huffman Coding ( VLC,Entropy)
Using Histogram probability 5 Steps
1. Find the histogram probabilities
2. Order the input probabilities(smalllarge)
3. Addition the 2 smallest
4. Repeat step 2&3, until 2 probability are left
5. Backward along the tree assign 0 and 1
20
Huffman Coding(cont)
Step 1 Histogram Probability 40302010
0 1 2 3
p0 = 20/100 = 0.2p1 = 30/100 = 0.3p2 = 10/100 = 0.1p3 = 40/100 = 0.4
p3 0.4p1 0.3p0 0.2p2 0.1
Step 2 Order
21
Huffman Coding(cont) Step 3,4 Add 2 smallest
2
0
1
3
p
p
p
p
1.0
2.0
3.0
4.0
3.0
3.0
4.0
6.0
4.04.0
6.0
Natural Code Probability Huffman Code
00 0.2 010
01 0.3 00
10 0.1 011
11 0.4 1
Step 5 assign 0 and 1
22
Huffman Coding(cont)
The original Image :average 2 bits/pixel The Huffman Code:average
3
02 )(log
iii ppEntropy
)4.0(log)4.0()1.0(log)1.0(
)3.0(log)3.0()2.0(log)2.0(
22
22
pixelbits /846.1
9.1)4.0(1)1.0(3)3.0(2)2.0(33
0
i
iia plL
23
Run-length encoding (RLE) is a very simple form of data compression in which runs of data (that is, sequences in which the same data value occurs in many consecutive data elements) are stored as a single data value and count, rather than as the original run. This is most useful on data that contains many such runs: for example, relatively simple graphic images such as icons, line drawings, and animations.
Run Length Encoding
24
Run-Length Coding
Counting the number of adjacent pixels with the same gray-level value
Used primarily for binary image Mostly use horizontal RLC
25
Run-Length Coding(cont)
Binary Image 8X8
00000000
00101111
01100100
01001110
00111110
00000110
00001111
00000000
horizontal
1st Row 8
2nd Row 4,4
3rd Row 1,2,5
4th Row 1,5,2
5th Row 1,3,2,1,1
6th Row 2,1,2,2,1
7th Row 4,1,1,2
8th Row 8
26
Example of RLE
Let us take a hypothetical single scan line, with B representing a black pixel and W representing white:
WWWWWWWWWWWWBWWWWWWWWWWWWBBBWWWWWWWWWWWWWWWWWWWWWWWWBWWWWWWWWWWWWWW If we apply the run-length encoding (RLE) data compression
algorithm to the above hypothetical scan line, we get the
following:12W1B12W3B24W1B14W
27
Lossy Compression
Allow a loss in the actual image data Can not recreated exactly original image Commonly the achievable compression is
mush more Such as JPEG
28
DM (Delta Modulation)
DPCM (Differential Pulse Code Modulation)
Predictive Coding
Common Predictive Coding
29
The system consists of an encoder and a decoder, each containing an identical predictor. As each successive pixel of the input image, is introduced to the encoder, the predictor generates the anticipated value of that pixel based on some number of past inputs. The output of the predictor is then rounded to the nearest integer.
The principle of Predictive Coding
30
Predictive coding model I
Input image
Predictor
n Symbol encoder
Symbol decoder
‘Output image
Predictor
nf̂
'nf
nf
'ˆnf
Compressed image
Input image
Predictor
n Symbol encoder
Symbol decoder
‘Output image
Predictor
nf̂
'nf
nf
'ˆnf
Compressed image
31
The predictor : ),,,(ˆknnnn fffFf 21
nnn ff ˆ
The symbol encoder : generate the next element of the compressed data stream
Decoder : perform the inverse of encoding
1
1
21 k
n
lkkkknnnn afafffFf ,),,,(ˆ
The linearity predictor :
Predictive coding model II
32
Delta Modulation I
Delta modulation (DM or Δ-modulation) is an analog-to-digital and digital-to-analog signal conversion technique used for transmission of voice information where quality is not of primary importance. DM is the simplest form of differential pulse-code modulation (DPCM) where the difference between successive samples is encoded into n-bit data streams. In delta modulation, the transmitted data is reduced to a 1-bit data stream.
33
Differential Pulse Code Modulation
Differential Pulse Code Modulation (DPCM) compares two successive analog amplitude values, quantizes and encodes the difference, and transmits the differential value.
34
Transform Coding I (DCT)
Transform coding is a type of data compression for "natural" data like audio signals or photographic images.
The transformation is typically lossy, resulting in a lower quality copy of the original input.
35
Transform Coding II
36
Transform Coding III
A transform coding system
Input image Forward
transform
Quantizer Symbol
encoder
Construct
subimages
Compressed
image
Merge
subimages
Symbol
decoder
Compressed
image Inverse
transform
37
BMP (Bitmap) - lossless
Use 3 bytes per pixel, one each for R, G, and B
Can represent up to 224 = 16.7 million colors
No entropy coding File size in bytes =
3*length*height, which can be very large
Can use fewer than 8 bits per color, but you need to store the color palette
Performs well with ZIP, RAR, etc.
38
GIF (Graphics Interchange Format)
Can use up to 256 colors from 24-bit RGB color space– If source image contains
more than 256 colors, need to reprocess image to fewer colors
Suitable for simpler images such as logos and textual graphics, not so much for photographs
39
JPEG (Joint Photographic Experts Group) - lossly
Most dominant image format today
Typical file size is about 10% of that of BMP (can vary depending on quality settings)
Unlike GIF, JPEG is suitable for photographs, not so much for logos and textual graphics
40
The name "JPEG" stands for Joint Photographic Experts Group, the name of the committee that created the standard. The group was organized in 1986, issuing a standard in 1992, which was approved in 1994 as ISO 10918-1. JPEG is a commonly used method of compression for photographic images. The degree of compression can be adjusted, allowing a selectable tradeoff between storage size and image quality.
Joint Picture Expert Group
41
JPEG2000
JPEG 2000 is a wavelet-based image compression standard. It was created by the Joint Photographic Experts Group committee in the year 2000 with the intention of superseding their original discrete cosine transform-based JPEG standard (created about 1991).
42
JPEG Encoding Steps
Preprocess image
Apply 2D forward DCT
Quantize DCT coefficients
Apply RLE, then entropy encoding
43
JPEG Block Diagram
FDCT
SourceImage
QuantizerEntropyEncoder
TableTable
Compressedimage data
DCT-based encoding
8x8 blocks
R
B
G
44
Image Compression- JPEG
Using the DCT, the entries in Y will be organized based on the human visual system.
The most important values to our eyes will be placed in the upper left corner of the matrix.
The least important values will be mostly in the lower right corner of the matrix.
Semi-
Important
Most
Important
Least
Important
45
JPEG
8 x 8 Pixels Image
46
Image Compression
Gray-Scale Example: Value Range 0 (black) --- 255 (white)
63 33 36 28 63 81 86 98
27 18 17 11 22 48 104 108
72 52 28 15 17 16 47 77
132 100 56 19 10 9 21 55
187 186 166 88 13 34 43 51
184 203 199 177 82 44 97 73
211 214 208 198 134 52 78 83
211 210 203 191 133 79 74 86
47
Image Compression
2D-DCT of matrix
-304 210 104 -69 10 20 -12 7
-327 -260 67 70 -10 -15 21 8
93 -84 -66 16 24 -2 -5 9
89 33 -19 -20 -26 21 -3 0
-9 42 18 27 -7 -17 29 -7
-5 15 -10 17 32 -15 -4 7
10 3 -12 -1 2 3 -2 -3
12 30 0 -3 -3 -6 12 -1
48
Image Compression
-304 210 104 -69 10 20 -12 0-327 -260 67 70 -10 -15 0 0 93 -84 -66 16 24 0 0 0 89 33 -19 -20 0 0 0 0 -9 42 18 0 0 0 0 0 -5 15 0 0 0 0 0 0 10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
As you can see, we save a little over half the original memory.
49
Reconstructing the Image
New Matrix and Compressed Image
55 41 27 39 56 69 92 106
35 22 7 16 35 59 88 101
65 49 21 5 6 28 62 73
130 114 75 28 -7 -1 33 46
180 175 148 95 33 16 45 59
200 206 203 165 92 55 71 82
205 207 214 193 121 70 75 83
214 205 209 196 129 75 78 85
50
Can You Tell the Difference?
Original Compressed
51
Image Compression
Original Compressed
52
Example - One everyday photo with file size of 2.76 MB
53
Example - One everyday photo with file size of 600 KB
54
Example - One everyday photo with file size of 350 KB
55
Example - One everyday photo with file size of 240 KB
56
Example - One everyday photo with file size of 144 KB
57
Example - One everyday photo with file size of 88 KB
58
Analysis
Near perfect image at 2.76M, so-so image at 88K Sharpness decreases as file size decreases Which file size is the best?
– No correct answer to this question– Answer depends upon how strict we are about image quality, what
purpose image is to be used for, and the resources available
59
Conclusion
Image compression is important Image compression has come a long way Image compression is nearly mature, but there
is always room for improvement
60
Video Compression
Video Data Size
1920x1080 1280x720 640x480 320x240 160x1201 sec 0.19 0.08 0.03 0.01 0.001 min 11.20 4.98 1.66 0.41 0.101 hour 671.85 298.60 99.53 24.88 6.22
1000 hours 671,846.40 298,598.40 99,532.80 24,883.20 6,220.80
size of uncompressed video in gigabytes
image size of video
1280x720 (1.77) 640x480 (1.33) 320x240 160x120
62
Video Bit Rate Calculation
width ~ pixels (160, 320, 640, 720, 1280, 1920, …)
height ~ pixels (120, 240, 480, 485, 720, 1080, …)
depth ~ bits (1, 4, 8, 15, 16, 24, …)
fps ~ frames per second (5, 15, 20, 24, 30, …)
compression factor (1, 6, 24, …)
width * height * depth * fps
compression factor= bits/sec
63
Effects of Compression
storage for 1 hour of compressed video in megabytes1920x1080 1280x720 640x480 320x240 160x120
1:1 671,846 298,598 99,533 24,883 6,2213:1 223,949 99,533 33,178 8,294 2,0746:1 111,974 49,766 16,589 4,147 1,037
25:1 26,874 11,944 3,981 995 249100:1 6,718 2,986 995 249 62
3 bytes/pixel, 30 frames/sec
64
Channel Bandwidths
65
Channel Bandwidth
66
Application Requirements
67
Source Video Formats
68
The Need for Video Compression
High-Definition Television (HDTV)– 1920x1080 – 30 frames per second (full motion)– 8 bits for each three primary colors (RGB)
Total 1.5 Gb/sec! Cable TV: each cable channel is 6 MHz
– Max data rate of 19.2 Mb/sec– Reduced to 18 Mb/sec w/audio + control …
Compression rate must be ~ 80:1!
69
Some figures – Uncompressed video -> big amount of data
Color picture 800x320 pix, 24 bits/pix -> 6.3 Mbit/sSDTV 720x480, 30Hz, 16 bits/pix -> 166 Mbit/sHDTV 1920x1080, 30Hz, 16 bits/pix -> 1Gbit/s
– Communication and storage capacities limitsCable or satellite bandwidth : 38 Mbit/sADSL : 1 to 8 Mbit/sDVD capacity : 5 to 8 GB
The Need for Video Compression
70
كاربرد
Video compression is now everywhere :– TV broadcasting over cable, satellite or terrestrial
networks,– CD-ROM, DVD, PC video storage,– Videophone and teleconferencing,– (VoD, IPTV),– Video over moInternet streaming biles.
71
H.264/SVC
SMPTE/VC1
2000’s1990’s1980’s1970’s1960’s
Standardization (1/2)Video codecs
Transform Coding 65/80
MC Prediction 72/89
Entropy Coding 49/76
H.261
MPEG-1
H.262/MPEG62
MPEG4 ASP
H.263
H.264/AVC
DVCPRO
1950’s
DPCM 52/80
Tech
nolo
gies
Sta
ndar
ds
Videophone 56Kb/s – 2Mb/s
CD-ROM 1-1.5Mb/s
Digital TV, DVD 4 to 80 Mb/s
Camcorder, VTR 25 to 50 Mb/s
Videophone 30 Kb/s
Video streaming & post-prod 30 Kb/s to 600Mb/s
Convergence of all video applications, digital cinema 30 Kb/s to 600Mb/s
Wavelet 85/--
72
Bit rate evolution
Mbi
t/s
2001
0
1
2
3
4
5
6
1995 1997 1999 2003 2005 2007
MPEG-2
1st generation encoders
1st generationencoders
2nd generationencoders
MPEG-4/H.264 AVC
MPEG-4 ASP
C. Ratio from 4:2:2
166
28
Bit rate evolution for SDTV Broadcast
3rd generation encoders(advanced Pre-processing)
2nd generation encoders(Stat-Mux + Rate control improvements)
200973
MPEG Compression
Compression through– Spatial– Temporal
74
Video Redundancies
Spatial Neighboring pixels in a frame are statistically related.
Temporal Pixels in consecutive frames are statistically related. One can achieve higher compression ratios by exploiting both spatial and temporal redundancies
75
Spatial Redundancy
Take advantage of similarity among most neighboring pixels
76
Spatial Redundancy Reduction
RGB to YUV – less information required for YUV (humans less sensitive to
chrominance) Macro Blocks
– Take groups of pixels (16x16) Discrete Cosine Transformation (DCT)
– Based on Fourier analysis where represent signal as sum of sine's and cosine’s
– Concentrates on higher-frequency values– Represent pixels in blocks with fewer numbers
Quantization– Reduce data required for co-efficients
Entropy coding– Compress77
Motion Compensation
Macro Block
Motion Vector
16 x 16
1 2 34 5 67 98
1
8 96
2 34 57
78
Video compression in MPEG-1&2 Spatial redundancy reduction (DCT example)
158 0 -1 0 0 0 0 0 -1 -1 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
139 144 149 153 155 155 155 155144 151 153 156 159 156 156 156150 155 160 163 158 156 156 156159 161 162 160 160 159 159 159159 160 161 162 162 155 155 155161 161 161 161 160 157 157 157162 162 161 163 162 157 157 157162 162 161 161 163 158 158 158
158 0 -1 -1 -1 -1 EOBzig-zag scan
1260 -1 -12 -5 2 -2 -3 1 -23 -17 -6 -3 -3 0 0 -1 -11 -9 -2 2 0 -1 -1 0 -7 -2 0 1 1 0 0 0 -1 -1 1 2 0 -1 1 1 2 0 2 0 -1 1 1 -1 -1 0 0 -1 0 2 1 -1 -3 2 -4 -2 2 1 -1 0
DCT
Quantisation
79
Spatial Redundancy Reduction
Zig-Zag Scan,Run-length
coding
Quantization• major reduction• controls ‘quality’
“Intra-FrameEncoded”
80
Loss of Resolution
Original (63 kb)
Low (7kb)
Very Low (4 kb)
81
Temporal Redundancy
Take advantage of similarity between successive frames
950 951 95282
Temporal Redundancy Reduction
83
Temporal Redundancy Reduction
84
Temporal Redundancy Reduction
frames are independently encoded P frames are based on previous I, P frames
– Can send motion vector plus changes B frames are based on previous and following I and P
frames– In case something is uncovered
Group of Pictures (GOP)
Starts with an I-frame Ends with frame right before next I-frame “Open” ends in B-frame, “Closed” in P-frame MPEG Encoding a parameter, but ‘typical’:
– I B B P B B P B B I– I B B P B B P B B P B B I
86
Question
When may temporal redundancy reduction be ineffective?
87
Answer
When may temporal redundancy reduction be ineffective?– Many scene changes– High motion
88
Non-Temporal Redundancy
Sometimes high motion
89
Typical Compress. Performance
Type Size Compression--------------------- I 18 KB 7:1 P 6 KB 20:1 B 2.5 KB 50:1Avg 4.8 KB 27:1
---------------------Note, results are Variable Bit Rate, even if frame rate is constant90
Inter and Intra coding
To exploit spatial redundancies within a
frame (Intra coding):
8x8 DCT, similar to JPEG To exploit temporal redundancies between
frame (Inter coding):
Motion Estimation
91
Frame Types
Two frame types:
Intra-frames (I-frames): I-frame provides an accessing point, it uses basically JPEG.
Inter-frames (P-frames): P-frames use from previous frame ("predicted"), so frames depend on eachother.92
93
Some video formats
The 4:2:2 format– Y sampling @ 13.5 MHz– C sampling @ 6.75 MHz– 8 bits per pixel– 720 active points per line– 576 lines active lines per image (2 fields) (625 lines)
and 480 active lines (525 lines) – Pixels are not square (e.g. for 480 lines, only 640 active points are needed -
VGA format)– Image size 720*576 or 720*480
The 4:2:0 format– Vertical chrominance resolution reduced by a factor 2
(average on two successive lines)
December, 20, 2006AV Compression / Alain Bouffioux 94
Some video formats
SIF format (Source Intermediate Format)Half the vertical & horizontal resolution of 4:2:0For 50Hz countries:– Luminance: 360*288– Chrominance: 180*120
CIF format (Common Intermediate Format)– Intermediate format used in videoconferencing
(communication between US & Europe)– resolution: 360*288 – Sampling frequency: 30 Hz
QCIF (Quarter CIF)– Half the vertical & horizontal resolution of CIF.
MPEG was an early standard for lossy compression of video and audio.
Development of the MPEG standard began in May 1988.
MPEG
95
96
MPEG Coding Performance
Decoding is easy– MPEG1 decoding in software on most platforms– Hardware decoders are widely available ($150/board)– Windows graphics accelerators with MPEG decoding
now entering market (e.g., Matrox, Diamond, …) Encoding is expensive
– Sequential software encoders are 20:1 real-time– Real-time encoders use parallel processing– Real-time hardware encoders are expensive (e.g.,
$12K-$50K for MPEG1 and $100K-$500K for MPEG2)– Hardware-assisted off-line MPEG1 encoders (3:1)
used for multimedia authoring at reasonable cost ($5k)
MPEG
MPEG1: low bitrate MPEG2: VCD, DVD MP3: MPEG2 profile 3, for music MPEG4: Network streaming MPEG7: Searching and indexing
98
MPEG Standards
MPEG-1 ~ 1-1.5Mbps (early 90s) - vhs quality (1992)– Frame encoding– For compression of 320x240 full-motion video at rates
around 1.15Mb/s– Applications: video storage (VCD)– CIF images, 4:2:0 sampling, 1.5 Mbs
99
MPEG Standards
MPEG-2 ~ 2-80Mbps (mid 90s) - broadcast quality (1994)– Frame and field encoding– For higher resolutions– Support interlaced video formats and a number of features
for HDTV– Address scalable video coding– Also used in DVD– CCIR 601 images, 4:2:2 sampling, 15 Mbs– Interlaced and progressive scanning
MPEG Today
MPEG4 ~ 9-40kbps (later 90s)– Around Objects, not Frames
– For very low bit rate video and audio coding
– Applications: interactive multimedia and video telephony– Lower bandwidth
MPEG-7– Provide a fast and efficient searching, filtering
– New standard
– Internet orientated
– VOP (Video Object Plane)
– Profiles and levels100
H.26x
H.261: first generation H.262 (MPEG2) H.263: video conference H.263++: enhancement H.26L: latest
VOD
Watch what you want when you want
Standardization (2/2)DVB Transport
20011995 1997 1999 2003 2005 2007 2009
DVB-S
DVB-C
DVB-S2
DVB-T
DVB-H
DVB-IPI in progress
Satellite TV
Cable
Mobile TV
Terrestrial TV
IPTV
103
Video on Demand
One video server Many video data Many clients Client want to watch at any time
Clients can send request to the server and request to watch a particular video. The server have to response by streaming the requested video to the client. We want to be able to support large number of clients, and clients should be able to watch at anytime.
104
Streaming
– Streaming media: the ‘real-time’ playing of a video-, audio- and/or datastream on a machine from the moment the first bytes come in.
– VoD such as YouTube, MSN Video, Google Video, Yahoo Video, CNN…
– 2006 April to December: MSN Video service Client-server mode Covering over 520 million streaming requests for
more than 59,000 videos.105
Streaming: live vs on-demand
archive realtime
unicast
multicast
VoD Event-driven
Event-driven
scheduled
Live audio and/or video streaming is a completely different sport from on-demand streaming.
VoD (Video on demand) is unicast streaming from an archive.
Scheduled (a tv like netcast) is usually multicast streaming from an archive
Live (realtime) streaming can be done both in unicast and multicast.
Streaming: live vs on-demand
107
Motivation
VoD such as YouTube, MSN Video, Google Video, Yahoo Video, CNN…
As the trend of increasing demands on such services and higher-quality videos, it becomes a costly service to provide.
108
109
Video-on-Demand Distribution Model
A client can tune in to receive any ongoing media delivery using its Set Top Box
True broadcast: Satellite and cable TV networks
VI DEO
SERVER
Multicast/ Broadcast Capable Network
STB STB STB
109
VoD servers support rewind function
Set-top box
TV
customer premise
VoD servers
Rewind TV
IP backbone Unicast
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Broadcast feed
Media databas
e
IP Streamer
Content Management Server
EPG metadata
Encoder
Encryption Server
VOD Server
Application Servers Cable
Modem Termination System
Optical Line
Termination
DSLAM
ADSL Modem/Rout
er
Optical Network
Termination
Cable Modem/Rout
er
Core & Aggregati
on Networks
STB
DSL (Copper)
DSL (Copper)
Optical Fiber
Optical Fiber
HFC (Coax)
HFC (Coax)
Content Provider
Service Provider Network Provider
CPE
Remote Management
Server
Subscriber Management and Billing
Server
Licence Server
RT Encoder
IPTV/VOD : Full Operator model
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What is IPTV?
The fundamentals– IPTV = Internet Protocol Television
Digital TV service delivered over a broadband network using the Internet Protoc0l
IPTV usually refers to TV services over a Network Operator’s quality controlled network.
Internet TV = IPTV over the public Internet
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One of the first One of the largest 150 TV channels 250,000 subscribers
IPTV Network
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Basic IPTV Structure
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Set Top Box – Home gateway (BST)
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IPTV Set-Top Box
Broadcast TV DVR Movies On Demand On Demand
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Other Services
VOD – Video on Demand– Watch what you want when you want
DVR– Record Live TV from your set top box
HD– High Definition TV. The evolution of Television
On line Gaming
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Title: The Hidden ChildDescription: Of the 1,600,000 Jewish children who lived in Europe before World War II, only 100,000 survived the Holocaust.
Title: Ralph Golzio interview on Paterson Silk Strike of 1913Description: Ralph Golzio was a teenager at the time of the Paterson Silk Strike of 1913.
Title: Sondra Gash interviewDescription: Evelyn Hershey of the American Labor Museum interviews
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Title: The Hidden ChildDescription: Of the 1,600,000 Jewish children who lived in Europe before World War II, only 100,000 survived the Holocaust.
Title: Ralph Golzio interview on Paterson Silk Strike of 1913Description: Ralph Golzio was a teenager at the time of the Paterson Silk Strike of 1913.
Title: Sondra Gash interviewDescription: Evelyn Hershey of the American Labor Museum interviews
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Selection of video found through search
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Start Time: 5:01 End Time: 8:34
Title: Amsterdam, The Netherlands
Note: City street in Amsterdam.
Indexed/Searchable: üPrivate NJVid Wide
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My Institution Only
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Amsterdam, The Netherlands
Annotate and Capture Start/End Times
Start and stop markers can be placed.
Metadata placed here.
Once an annotation is saved it will appear in the videos timeline.
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