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2

Lecture No. 39

3

Presentation Formatting

Outline

Presentation FormattingData compression

5

Presentation Formatting

• Data types we consider
  • Integers– Floats– Strings– Arrays– Structs
    • Types of data we do not consider
      • Images– Video– Multimedia documents

6

Difficulties

• Representation of base types
  • Floating point: IEEE 754 versus non-standard– Integer: big-endian vs little-endian (e.g., 34,677,374)
    • Compiler layout of structures

(126)

(34)

(17)

(2)

00000010

Big-endianLittle- endian

(2)

(17)

(34)

(126)

Low address

High address

0

0

111111

0 0

0 0 1

0 0

1

0 0

0

01

001

0 0

0 0 1

0 0

1

0 0

0

01

001

0 0 0

000 0

1

0

0 1 1 1 1 1 1

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Taxonomy

• Conversion Strategy
  • Canonical intermediate form– Receiver-makes-right (an

N

x

N

solution)

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Taxonomy (cont)

• Tagged versus untagged data

type =

INT

len = 4

value = 417892

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eXternal Data Representation

(XDR)

• Defined by Sun for use with SunRPC• C type system (without function

pointers)

• Canonical intermediate form• Untagged (except array length)• Compiled stubs

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#define MAXNAME 256;#define MAXLIST 100;struct item {

int

count;

char

name[MAXNAME];

int

list[MAXLIST];

**};bool_txdr_item(XDR xdrs, struct item ptr){

return(xdr_int(xdrs, &ptr->count) &&

xdr_string(xdrs, &ptr->name, MAXNAME) &&xdr_array(xdrs, &ptr->list, &ptr->count,

MAXLIST, sizeof(int), xdr_int));

}

Count

Name J

O

3

7

H

N

S

O

N

List

3

4 9 7

2 6 5

8 321

Example Code (XDR)

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ASN.1 BER Representation

length

0

k

1

k containing

length

(a) (b)

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Network Data Representation (NDR)

• Defined by DCE• Essentially the C type

system

• Receiver-makes-right

(architecture tag)

• Individual data items

untagged

• Compiled stubs from IDL• 4-byte architecture tag
• IntegerRep
• 0 = big-endian• 1 = little-endian
• CharRep
• 0 = ASCII• 1 = EBCDIC
• FloatRep
• 0 = IEEE 754• 1 = VAX• 2 = Cray• 3 = IBM

IntegrRep 0

4

8

16

24

31

FloatRep

CharRep

Extension 1

Extension 2

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Compression Overview

• Encoding and Compression
  • Huffman codes
    • Lossless
      • Data received = data sent– Used for executables, text files, numeric data
        • Lossy
          • Data received is not equal to data sent– Used for images, video, audio

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Lossless Algorithms

• Run Length Encoding (RLE)
  • Example: AAABBCDDDD encoding as

3A2B1C4D

• Good for scanned text (8-to-

compression ratio)

• Can increase size for data with variation

(e.g. some images)

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Dictionary-Based Methods

Build dictionary of common terms

• Variable length strings

Transmit index into dictionary for each term

Lempel-Ziv (LZ) is the best-known example

Commonly achieve 2-to-1 ration on text

Variation of LZ used to compress GIF images

• First reduce 24-bit color to 8-bit color– Treat common sequence of pixels as terms in

dictionary

• Not uncommon to achieve 10-to-1 compression (

x

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Image Compression

JPEG: Joint Photographic Expert Group (ISO/ITU)

Lossy still-image compression

Three phase process

• Process in 8x8 block chunks (macro-block)– Grayscale: each pixel is three values (YUV)– DCT: transforms signal from spatial domain into and equivalent

signal in the frequency domain (loss-less)

Sourceimage – Apply a quantization to the results (lossy)– RLE-like encoding (loss-less)

JPEG compression

DCT

Quantization

Encoding

Compressed

image