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EEE 508 - Digital Image & Video Processing and Compression
Introduction
Prof. Lina KaramSchool of Electrical, Computer, & Energy Engineering
Arizona State [email protected]
http://lina.faculty.asu.edu/eee508/
Why Image and Video ?
Sample image-and video-based applications • Entertainment • Communications• Medical imaging• Security• Monitoring• Visual sensing and control
2
Basic Imaging System
Copyright 2007 ‐2012 by Lina J. Karam 3
CAMERA Imaging Device
Imaged Scene
DIGITIZER STORAGE PROCESSDisplay, Analysis, Enhancement, Restoration, Compression for transmission
Sampling + Quantization
Compression
Colored lights from scene are captured into red, green, and blue pixels (picture elements)
Scene viewed through “color” filters that separate the image into 3 color components
Digital camera systems contain optics that image light onto sensors typically a CCD array with filters
x
y
z
Basic Imaging System
Copyright 2007‐2012 by Lina J. Karam 4
CAMERA Imaging Device
Imaged Scene
DIGITIZER STORAGE PROCESSDisplay, Analysis, Enhancement, Restoration, Compression for transmission
Sampling + Quantization
Compression
Quality of captured image depends on imaging optics and electronics, “color” filter characteristics, digitization, and processing
x
y
z
z
Basic Imaging System
Copyright 2007 ‐2012 by Lina J. Karam 5
CAMERA Imaging Device
Imaged Scene
DIGITIZER STORAGE PROCESSDisplay, Analysis, Enhancement, Restoration, Compression for transmission
Compression
High-end digital cameras make use of dichroic filters to split the light into, red, green, and blue components
• Beam splitter is used to split light into three beams that are directed through filters that filter out all but one color for each chip (“dichroic” indicates that 2 out of the 3 colors are filtered).
• Each color component is imaged separately onto an array of sensors: one chip “sees” red (R), one “sees” green (G), and one “sees” blue (B).
• Three values (R, G, B) captured at each pixel position
x
y
z
Basic Imaging System
Copyright 2007‐2012 by Lina J. Karam 6
CAMERA Imaging Device
Imaged Scene
DIGITIZER STORAGE PROCESSDisplay, Analysis, Enhancement, Restoration, Compression for transmission
Sampling + Quantization
Compression
Common digital cameras have a single imaging element (typically one CCD chip) and make use of tiled Color Filter Array (CFA)
x
y
z
Light from scene
CFA
Image Sensor
Bayer CFAEach captured pixel is eitherGreen (G), Red (R), or Blue(B).Interpolation is used to recover(R,G,B) values at each pixel.
Digitization: Sampling and Quantization
Copyright 2007‐2012 by Lina J. Karam 7
CAMERA Imaging Device
Imaged Scene
DIGITIZER STORAGE PROCESSDisplay, Analysis, Enhancement, Restoration, Compression for transmission
Sampling + Quantization CompressionI(x,y;t)
I(n1,n2;n3)x
y
• Original Imaged Scene : analog (continuous in space and time) I(x,y;t) for video and I(x,y) for still imageI: image intensity and color at position (x,y) and at time t
• Digitized sensed image/video: digital (sampled in space and time, plus discrete amplitudes) I(n1,n2;n3) for video and I(n1,n2) for a still imageI: image intensity and color at integer sample position (n1,n2) and
integer time index n3
t
Basic Imaging System
Copyright 2007‐2012 by Lina J. Karam 8
CAMERA Imaging Device
Imaged Scene
DIGITIZER STORAGE PROCESSDisplay, Analysis, Enhancement, Restoration, Compression for transmission
Sampling + Quantization
Compression
x
• I(s1,s2,s3,t) : ANALOG SIGNALI : real value or vector of real values(s1,s2 ,s3,t) : set of real continuous space (time) variables
• I(n1,n2, n3,n4) : DISCRETE SIGNAL (DIGITAL)I : discrete (quantized) real or integer value(n1,n2,n3,n4 ) : set of integer indices
z
S3 or z
S1or x
t
S2 or y
9#EEE 508 - Lecture 1 9
• Sampled Black & White Photograph: I(n1,n2)I (n1,n2) scalar indicating pixel intensity at location (n1,n2) For example: I = 0 Black
I = 1 White0 < I < 1 In-between
• Sampled color video/TV signal
IR(n1, n2, n3) IR(n1, n2, n3 , n4) 2D TV: IG(n1, n2, n3) ; 3D TV: IG(n1, n2, n3 , n4)
IB(n1, n2, n3) IB(n1, n2, n3 , n4)
Examples
Digitization: Sampling and Quantization
Copyright 2007‐2012 by Lina J. Karam 10
Video Sampling • Temporal sampling affects frame (image) rate and perceived
motion quality.– 50 to 60 frames per second produce smooth apparent motion– 25 (PAL) or 30 (NTSC) frames per second is standard for
television pictures; interlacing can be used to improve the appearance of motion
• Frame rate can be referred to as temporal resolution.
Digitization: Sampling and Quantization
Copyright 2007‐2012 by Lina J. Karam 11
Video Sampling • Progressive and Interlaced Sampling
– Progressive sampling: all lines (rows) in a frame are sampled
– Interlaced sampling: alternate between sampling the odd rows (odd field) for one frame followed by the sampling the even rows (even field) for next frame
Odd or Top Field Even or Bottom Field
Spatial ResolutionA digital image is represented as a rectangular array of picture elements (pixels or pels).
Spatial resolution commonly refers to the number of pixels in the horizontal and vertical directions.
Copyright 2007‐2012 by Lina J. Karam 12
503 pixels
365pixels
503x365 pixelsTotal pixels = 183,595
Spatial Resolution
Copyright 2007‐2012 by Lina J. Karam 13
Video Formats based on Resolution1920
1080High Definition (HDTV) 2 Mega pixels
1280
720High Definition (HDTV) 1 Mega pixels
720
480480i SDTV, 345 Kilo pixels
352
288
176
144CIF, 101 Kpixels
QCIF
576SDTV, 415 Kilo pixels
Spatial Resolution
Copyright 2007‐2012 by Lina J. Karam 14
4CIF: 704x576
CIF: 352x288
QCIF: 176x144
SCIF: 128x96
Spatial ResolutionChoice of frame resolution depends on application and on available storage and transmission capacity.
Perceived resolution refers to the maximum number of line pairs that can be resolved on the display screen or to the smallest details that can be resolved.
• Depends on viewing distance.• Depends on display
Display resolution is commonly expressed in pixels per inch.
Copyright 2007‐2012 by Lina J. Karam 15
Aspect RatioAspect ratio is the ratio of the image’s width to its height.
Copyright 2007 ‐2012 by Lina J. Karam 16
SDTV Video4:3
1.33:1
Widescreen SDTVHDTV16:9
1.78:1
720
480
1280
720
Full HDTV
1.78:1
1920
1080
17#EEE 508 - Lecture 1 17
How do we process images?
• Exploit visual perception properties• Use/Develop image/video processing
(computer vision) algorithms• Use DSP concepts as tools
18#EEE 508 - Lecture 1 18
How many possible images are there?
We represent pixels as amplitude values (gray scale). 256 levels
1 0
128 levels
1 0
64 levels
1 0
32 levels
1 0
How much to sample (quantize) the gray scale?Humans can distinguish in the order of 100 levels of gray.
19#EEE 508 - Lecture 1 19
How many possible images are there?An image has pixels and dimensions, say 200x200 and assume 64
pixel values (64 gray levels).• A 1x1 image → about 64 images• A 1x2 image → about (64)2 images• A 200x200 image → about (64)40000 images• A large but finite number due to human perceptive
properties.