sampling and quantization notes in image process.ppt

image processing

1. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Digital Image
Processing
Sampling and Quantization

2. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
key stages in digital image processing
Sampling : related to coordinates values – Digitizing the
coordinate values
(Nyquist frequency)
Quantization : related to intensity values – Digitizing the
amplitude values
In order to become suitable for digital processing, an image function f(x,y)
must be digitized both spatially and in amplitude.
Typically, a frame grabber or digitizer is used to sample and quantize the
analogue video signal.
Hence in order to create an image which is digital, we need to covert
continuous data into digital form. There are two steps in Image
digitization:

3. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
The sampling rate determines the spatial resolution of the digitized image,
while the quantization level determines the number of grey levels in the
digitized image.
A magnitude of the sampled image is expressed as a digital value in image
processing.
The transition between continuous values of the image function and its
digital equivalent is called quantization.
The number of quantization levels should be high enough for human
perception of fine shading details in the image.
The occurrence of false contours is the main problem in image which has
been quantized with insufficient brightness levels.

4. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
© 2002 R. C. Gonzalez & R. E. Woods
y (intensity values)
Generating a digital image.
(a) Continuous image. (b)
A scaling line from A to B
in the continuous image,
used to illustrate the
concepts of sampling and
quantization. (c) sampling
and quantization. (d)
Digital scan line.
ab
cd

5. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
© 2002 R. C. Gonzalez & R. E. Woods
(a) Continuous image
projected onto a sensor
array. (b) Result of image
sampling and
quantization.
ab

6. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
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7. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Sampling
1024
512
256
128
64
32

8. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Sampling
1024 512 256
128 64 32

9. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Quantization
8-bit 7-bit 6-bit 5-bit
4-bit 3-bit 2-bit 1-bit

10. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Resizing Images
Zooming : Creating new pixel locations
Assigning gray-level values to these locations
Solution: Interpolation

11. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
3 main type of 2D Interpolations :
Nearest neighbor interpolation
Bilinear interpolation
Bicubic interpolation

12. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Image interpolation occurs when you resize or distort your image from
one pixel grid to another.
Image resizing is necessary when you need to increase or decrease the
total number of pixels, whereas remapping can occur when you are
correcting for lens distortion or rotating an image.
Zooming refers to increase the quantity of pixels, so that when you
zoom an image, you will see more detail.

13. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Common interpolation algorithms can be grouped into two categories:
adaptive and non-adaptive.
Adaptive methods change depending on what they are interpolating,
whereas non-adaptive methods treat all pixels equally.
Non-adaptive algorithms include: nearest neighbor, bilinear, bicubic,
spline, sinc, lanczos and others.
Adaptive algorithms include many proprietary algorithms in licensed
software such as: Qimage, PhotoZoom Pro and Genuine Fractals.

14. Gholamreza Anbarjafari, PhD
Video Lecturers on Digital Image Processing
Nearest
Neighbor
64 1024128 1024
Bilinear
Bicubic