lab #5-6 follow-up: more python; images images ● a signal (e.g. sound, temperature infrared sensor...

Lab #5-6 Follow-Up:

More Python; Images

Images● A signal (e.g. sound, temperature infrared

sensor reading) is a single (one-

dimensional) quantity that varies over time.● An image (picture) can be thought of as a

two-dimensional quantity that varies over

space.● Otherwise, the computations for sounds

and images are about the same!

Black-and-White Images

● Eventually we get down to a single point

(pixel, “picture element”) whose color we

need to represent.● For black-and-white images, two basic

options–Halftone: Black (0) or white (1); having lots of

points makes a region look gray–Grayscale: Some set of values (2N, N typically

= 8) representing shades of gray.

● To turn a b/w image into a grid of pixels, imagine

running a stylus horizontally along the image:

● This gives us a one-dimensional function showing

gray level at each point along the horizontal axis:

● Doing this repeatedly with lots of evenly-spaced

scan lines gives us a grayscale map of the image:

● Height =

grayscale value

● Spacing between lines

= spacing within line

Color● Light comes in difference wavelengths (frequencies):

● Like a sound, energy from a given light source (e.g., star) can be described by its wavelength (frequency) spectrum : the amount of each wavelength present in the source.

Spectrum of vowel “ee”

Frequency (Hz)

Ene

rgy

(dB

)

Spectrum of star ICR3287

Wavelength (nm)

Inte

nsity

● Light striking our eyes is focused by a lens and projected onto the retina.

● The retina contains photoreceptors (sensors) called cones that respond to different wavelengths of light: red, green, and blue.

● So retina can be thought of as a transducer that inputs light and outputs a three-dimensional value for each point in the image: [R, G, B], where R, G, and B each fall in the interval (0,1).

● So we can represent any color image as three “grayscale” images:

=

,R

,G B

Digital Sampling of Images● Same questions arise for sampling images as for

sound:

–Sampling Frequency (how often to sample)–Quantization (# of bits per sample)

● With images, “how often” means “how many times per linear unit (inch, cm) – a.k.a. resolution

(dpi)● Focus on quantization–Each sample is either an RGB triple or an

index into a color map–With too few bits, we lose gradual shading

Color Maps● With 8 bits per color, 3 colors per pixel, we get 24 bits per pixel: 224

≈ 17 million distinct colors● We can actually get away with far fewer, using color maps● Each pixel has a value that tells us what row in the color map to use● Color map rows are R, G, B values:

Color Maps (in Matlab)>> colormap

ans =

0 0 0.5625 0 0 0.6250 ... 0.4375 1.0000 0.5625 0.5000 1.0000 0.5000 ...

0.5625 0 00.5000

0 0>> size(colormap)

ans = 64 3

Quantization ProblemsWith too few bits, we lose gradual shading:

5 bits

1 bit

2 bits

3 bits

4 bits

6 bits

7 bits

8 bits

Sampling and Storing Images in Files

• Cameras / scanners usually output images in one of several formats– JP(E)G (Joint Photographic Experts Group)–GIF (Graphics Interchange Format)–PNG (Portable Network Graphics)–TI(F)F (Tagged Image File Format)

• As with sound formats, main issues in image formats are compression scheme (how images are stored and transmitted to save space/time) and copyright

JPEG Compression

• Images contain lots of redundant information

• Images contain lots of redundant information

530 x 279 x 3 = 443610 bytes (assuming 3 bytes per pixel)

MOSTLY BLUE

MOSTLY BROWN

MOSTLY GREEN

MOSTLY WHITE

Homebrew Compression

530, 84, 151,129,235

530, 72, 255,255,255

530, 66, 0,128,64

530, 60, 128,64,0

Homebrew Compression

Homebrew Compression• Assuming two bytes per number 4*5*2 = 40 bytes

• 443610 / 40 = factor of 11000!

530, 84, 151,129,235

530, 72, 255,255,255

530, 66, 0,128,64

530, 60, 128,64,0

Lossy Compression

• This sort of compression is lossy: reduces size but loses resolution

• JPEG offers a better lossy compromise – typically, around factor 10 without noticeable loss in quality

1. Convert each RGB pixel into a form in which brightness (“Luminance”) has its own value, and use just two values (“Chrominance”, red or blue) for color.

The JPEG Algorithm

http://en.wikipedia.org/wiki/YCbCr

2. Quantize the chroma data by a factor of two, because the eye is more sensitive to brightness than to color.

The JPEG Algorithm

E.g.:

83 0101001198 01100010123 01111011200 1100100044 00101100

01010000 8001100000 9601110000 11211000000 19200100000 32

3. Break the transformed image down into blocks of 8x8 pixels. We can represent each block as the weighted sum of various patterns, or frequency components,

which will differ from block to block. The set of 64 weights becomes the new representation of the block.

The JPEG Algorithm

http://en.wikipedia.org/wiki/JPEG#Discrete_cosine_transform

4. Quantize the frequency weights, giving more precision (bits) to the low-frequency components: again, because the eye is more sensitive to low-frequency variations (big, slowly changing patterns).

JPEG Quality refers to how much we allow the high-frequency components to persist.

The JPEG Algorithm

JPEG Quality

Re-compressed with Quality = 0:

5. Compress the quantized block weights using a lossless algorithm like Huffman Coding

The JPEG Algorithm

• The basic idea: Values that occur more often should be given shorter codes.

• In the previous sentence:

Huffman Coding

So maybe use this code (ignoring space):

Values that occur …11110001011000100001001010010011101 …

• Uses a tree structure to determine when we’re at the end of a code item:

Huffman Code

http://www.personal.kent.edu/~rmuhamma/Algorithms/MyAlgorithms/Greedy/huffman.htm

• All digital data (MS Word files, JPEG images, MP3 songs, MP4 videos) is just a sequence of numbers

• How we interpret those numbers depends on the program we’re using to look at the data

• Compressions schemes (JPEG, MP3) rely on ignoring the numbers that don’t make as much of a difference in our perception of the image, song, etc.

• JPEG is not a single algorithm; it’s a grab-bag of techniques that yield good results in combination

Images: Summary