Bài giảng Digital Signal Processing - Chapter 0: Introduction

al 
signal 
analog 
signal 
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Digital Signals Everywhere! 
• Fax machines: transmission of binary images 
• Digital cameras: still images 
• iPod / iPhone & MP3 
• Digital camcorders: video sequences with audio 
• Digital television broadcasting 
• Compact disk (CD), Digital video disk (DVD) 
• Personal video recorder (PVR, TiVo) 
• Images on the World Wide Web 
• Video streaming & conferencing 
• Video on cell phones, PDAs 
• High-definition televisions (HDTV) 
• Medical imaging: X-ray, MRI, ultrasound, telemedicine 
• Military imaging: multi-spectral, satellite, infrared, microwave 
Digital Bit Rates 
• A picture is worth a thousand words? 
• Size of a typical color image 
– For display 
• 640 x 480 x 24 bits = 7372800 bits = 92160 bytes 
– For current mainstream digital cameras (5 Mega-pixel) 
• 2560 x 1920 x 24 bits = 117964800 bits = 14745600 bytes 
– For an average word 
• 4-5 characters/word, 7 bits/character: 32 bits ~= 4 bytes 
• Bit rate: bits per second for transmission 
– Raw digital video (DVD format) 
• 720 x 480 x 24 x 24 frames: ~200 Mbps 
– CD Music 
• 44100 samples/second x 16 bits/sample x 2 channels ~ 1.4 Mbps 
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Reasons for Compression 
• Digital bit rates 
– Terrestrial TV broadcasting channel: ~20 Mbps 
– DVD: 10...20 Mbps 
– Ethernet/Fast Ethernet: <10/100 Mbps 
– Cable modem downlink: 1-3 Mbps 
– DSL downlink: 384...2048 kbps 
– Dial-up modem: 56 kbps max 
– Wireless cellular data: 9.6...384 kbps 
• Compression = Efficient data representation! 
– Data need to be accessed at a different time or location 
– Limited storage space and transmission bandwidth 
– Improve communication capability 
Personal Video Recorder (PVR) 
MPEG2 Quality 
Best 7.7 Mbps 
High 5.4 Mbps 
Medium 3.6 Mbps 
Basic 2.2 Mbps 
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Sound Fundamentals 
 Sound waves: 
vibrations of air 
particles 
 Fluctuations in air 
pressure are picked up 
by the eardrums 
 Vibrations from the 
eardrums are then 
interpreted by the 
brain as sounds 
Sound Waves: 1-D signals 
 Frequency 
 How fast the air pressure fluctuates 
 High pitch, low pitch 
 Volume 
 Amplitude of the sound wave 
 How loud the sound is 
 Phase 
 Determine temporal and spatial 
localization of the sound wave 
) cos()( iiii tAtx  
volume 
frequency 
phase 
)()( txtx
i
i
envelope 
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Frequency Spectrum for Audio 
f (Hz) 
0 20k 10k 
Human Auditory System 
 20Hz-20kHz 
f (Hz) 
0 20k 10k 
 FM Radio Signals 
 100Hz-12kHz 
f (Hz) 
0 20k 10k 
 AM Radio Signals 
 100Hz-5kHz 
f (Hz) 
0 20k 10k 
 Telephone Speech 
 300Hz-3.5kHz 
kHzfkHzf sampling 6.63.3max 
Speech Signals 
ph - o - n - e - t - i - c - ia - n 
 Main useful frequency range of human voice: 
 300 Hz – 3.4 kHz 
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Music Signals 
     
       tttt
ttttx


13cos17.011cos12.09cos5.07cos14.0
5cos5.03cos75.0cos)(


frequency lfundamenta2  f
Harmonics in Music Signals 
 The spectrum of a single note from a 
musical instrument usually has a set of 
peaks at harmonic ratios 
 If the fundamental frequency is f, there 
are peaks at f, and also at (about) 2f, 3f, 
4f 
 Best basis functions to capture speech 
& music: cosines & sines 
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Multi-Dimensional Digital Signals 
• Images: 2-D digital signals 
pixel 
 or 
 pel 
• Video Sequences: 3-D digital signals, a 
collection of 2-D images called frames 
x 
y 
t 
black 
 p=0 
 gray 
p=128 
white 
p=255 
colors: 
combination 
of RGB 
Color Spaces: RGB & YCrCb 
• RGB 
– Red Green Blue, typically 8-bit per sample for each color plane 
• YCrCb 
– Y: luminance, gray-scale component 
– Cr & Cb: chrominance, color components, less energy than Y 
– Chrominance components can be down-sampled without much 
aliasing 
– YCrCb, also known as YPrPb, is used in component video 
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128
128
16
439.0291.0148.0
071.0368.0439.0
098.0504.0257.0
C
C
Y
B
R
B
G
R
 Y 
sample Cr, Cb 
sample 
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Another Color Space: YUV 
• YUV is another popular color space, similarly to YCrCb 
– Y: luminance component 
– UV: color components 
– YUV is used in PAL/NTSC broadcasting 
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B
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100.0515.0615.0
436.0289.0147.0
114.0587.0299.0
V
U
Y
Y: 176 x 144 
U: 88 x 72 V: 88 x 72 
Popular Signal Formats 
• CIF: Common Intermediate Format 
– Y resolution: 352 x 288 
– CrCb/UV resolution: 176 x 144 
– Frame rate: 30 frames/second progressive 
– 8 bits/pixel(sample) 
• QCIF: Quarter Common Intermediate Format 
– Y resolution: 176 x 144 
– CrCb/UV resolution: 88 x 72 
– Frame rate: 30 frames/second progressive 
– 8 bits/pixel (sample) 
• TV – NTSC 
– Resolution: 704 x 480, 30 frames/second interlaced 
• DVD – NTSC 
– Resolution: 720 x 480, 24 – 30 frames/second progressive 
Y 
Cr 
Cb 
Y 
Cr 
Cb 
Frame 
 n 
Frame 
 n+1 
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High-Definition Television (HDTV) 
• 720i 
– Resolution: 1280 x 720, interlaced 
• 720p 
– Resolution: 1280 x 720, progressive 
• 1080i 
– Resolution: 1920 x 1080, interlaced 
• 1080p 
– Resolution: 1920 x 1080, progressive 
Interlaced 
 Video 
 Frame 
odd field 
even field 
Examples of Still Images 
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Examples of Video Sequences 
Frame 1 51 71 91 111 
• Observations of Visual Data 
– There is a lot of redundancy, correlation, strong structure within 
natural image/video 
– Images 
• Spatial correlation: a lot of smooth areas with occasional edges 
– Video 
• Temporal correlation: neighboring frames seem to be very similar 
Digital Signal Processing 
 Telephony: transmission of information in 
digital form via telephone lines, modem 
technology, mobile phone. 
 4. DSP applications-Communications 
28 Introduction 
 Encoding and decoding of the 
information sent over physical 
channels (to optimize 
transmission, to detect or 
correct errors in transmission) 
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Digital Signal Processing 
Radar and sonar: 
 4. DSP applications-Radar 
29 Introduction 
 Target detection: 
position and 
velocity estimation 
 Tracking 
Digital Signal Processing 
 Analysis of biomedical signals, diagnosis, patient monotoring, 
preventive health care, artificial organs. 
 4. DSP applications-Biomedical 
30 Introduction 
 Examples: 
 Electrocardiogram (ECG) signal provides 
information about the condition of the 
patient’s heart. 
 Electroencephhalogram (EEG) signal 
provides information about the 
activity of the brain. 
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Digital Signal Processing 
Noise reduction: reducing 
background noise in the sequence 
produced by a sensing device (a 
microphone). 
 4. DSP applications-Speech 
31 Introduction 
 Speech recognition: differentiating 
between various speech sounds 
 Synthesis of artificial speech : 
text to speech systems 
Digital Signal Processing 
 Content based image retrieval-
browsing, searching and retrieving 
images from database. 
 4. DSP applications-Image Processing 
32 Introduction 
 Image enhancement 
 Compression: reducing the 
redundancy in the image data to 
optimize transmission/storage 
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Digital Signal Processing 
 Generation storage and transmission 
of sound, still images, motion 
pictures. 
 4. DSP applications-Multimedia 
33 Introduction 
 Digital TV 
 Video conference 
Digital Signal Processing 
 The Journey 
34 Introduction 
“ Learning digital signal processing is not something 
you accomplish; it’s a journey you take”. 
R.G. Lyons, Understanding Digital Signal Processing 
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Digital Signal Processing 
 5. Advantages of digital 
 over analog signal processing 
35 
 A digital programmable system allows flexibility in reconfiguring the 
DSP operations simply by changing the program. 
 A digital system provides much better control of accuracy 
requirements. 
 Digital signals are easily stored. 
 DSP methods allow for implementation of more sophisticated signal 
processing algorithms. 
 Limitation: Practical limitations of DSP are the quantization errors 
and the speed of A/D converters and digital signal processors -> not 
suitable for analog signals with large bandwidths. 
Introduction 
Digital Signal Processing 
 Course overview 
36 Introduction 
 Introduction to Digital Signal Processing (3 periods) 
Mid-term Exam 
 Fourier transform & FFT Algorithm (9 periods) 
 Sampling and reconstruction, quantization (6 periods) 
 Analysis of linear time invariant systems (LTI)(3 periods) 
 Finite Impulse Response (FIR) of LTI systems (3 periods) 
 Z-transform and its applications to the analysis of linear systems (6 
periods) 
 Digital filter realization(3 periods) 
 FIR and IIR filter designs (9 periods) 
Final Exam 
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Digital Signal Processing 
 Text books: 
[1] S. J. Orfanidis, Introduction to Signal Processing, Prentice –Hall 
Publisher 2010. 
[2] J. Proakis, D. Manolakis, Introduction to Digital Signal 
Processing, Macmillan Publishing Company, 1989. 
 References 
37 Introduction 
 Reference books: 
[3] V. K. Ingle, J. Proakis, Digital Signal Processing Using Matlab, 
Cengage Learning, 3 Edt, 2011. 
Digital Signal Processing 
 Learning outcomes 
38 Introduction 
 Understand how to convert the analog to digital signal 
 Be able to design and implement FIR and IIR filters. 
 Have a thorough grasp of signal processing in linear time-invariant 
systems. 
 Understand the z-transform and Fourier transforms in analyzing 
the signal and systems. 
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Digital Signal Processing 
 Assessment 
39 Introduction 
 Mid-term exam: 40% 
 Final exam: 60%