Image Compression

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DEPARTMENT OF COMPUTER SCIENCE UNIVERSITY OF
JOENSUU JOENSUU, FINLAND

• Image Compression
• Lecture 1
• Introduction
• Alexander Kolesnikov

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What is Data and Image Compression?

• Data compression is the art and science of
  representing information in a compact form.
• Data is a sequence of symbols taken from a
  discrete alphabet.
• Still image data, that is a collection of 2-D
  arrays (one for each color plane) of values
  representing intensity (color) of the point in
  corresponding spatial location (pixel).

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Why do we need Image Compression?

• Still Image
• One page of A4 format at 600 dpi is gt 100 MB.
• One color image in digital camera generates 10-30
  MB.
• Scanned 3?7 photograph at 300 dpi is 30 MB.
• Digital Cinema
• 4K?2K?3 ?12 bits/pel 48 MB/frame or 1 GB/sec
• or 70 GB/min.

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Why do we need Image Compression?
1) Storage 2) Transmission 3) Data access
1990-2000 Disc capacities 100MB -gt 20 GB (200
times!) but seek time 15 milliseconds ? 10
milliseconds and transfer rate 1MB/sec -gt2
MB/sec. Compression improves overall response
time in some applications.
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Source of images

• Image scanner
• Digital camera
• Video camera,
• Ultra-sound (US), Computer Tomography (CT),
• Magnetic resonance image (MRI), digital X-ray
  (XR),
• Infrared.
• etc.

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Image types
Why do we need special algorithms for images?
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Binary image 1 bit/pel
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Grayscale image 8 bits/pel
Intensity 0?255
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Parameters of digial images
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True color image 38 bits/pel
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RGB color space
Red Green Blue
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YUV color space
Y U
V
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RGB ? YUV
R, G, B -- red, green, blue Y -- the luminance
U,V -- the chrominance components Most of the
information is collected to the Y component,
while the information content in the U and V
is less.
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Palette color image
Look-up-table
R,G,B LUTIndex Example 64,64,0 LUT98
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Image types
Why do we need special algorithms for images?
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Why we can compress image?

• Statistical redundancy
• 1) Spatial correlation
• a) Local - Pixels at neighboring locations have
  similar intensities.
• b) Global - Reoccurring patterns.
• 2) Spectral correlation between color planes.
• 3) Temporal correlation between consecutive
  frames.
• Tolerance to fidelity
• 1) Perceptual redundancy.
• 2) Limitation of rendering hardware.

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Lossy vs. Lossless compression
Lossless compression reversible, information
preserving text compression algorithms,
binary images, palette images Lossy
compression irreversible grayscale,
color, video Near-lossless compression
medical imaging, remote
sensing. 1) Why do we need lossy compression? 2)
When we can use lossy compession?
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Lossy vs. Lossless compression
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Rate measures
Bitrate
bits/pel
Compression ratio
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Distortion measures
Mean average error (MAE)
Mean square error (MSE)
Signal-to-noise ratio (SNR)
(decibels)
Pulse-signal-to-noise ratio (PSNR)
(decibels)
A is amplitude of the signal A 28-1255 for
8-bits signal.
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Other issues

• Coder and decoder computation complexity
• Memory requirements
• Fixed rate or variable rate
• Error resilience
• Symmetric or asymmetric
• Decompress at multiple resolutions
• Decompress at various bit rates
• Standard or proprietary

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Contents

• Why do we need to compress images?
• Image types
• Parameters of digital images
• Lossless vs. Lossy compression
• Rate, Distortion, etc.