Image Compression Standards-Multimedia Applications-Lecture Slides, Slides of Multimedia Applications

This lecture was delivered by Dr. Paresh Sapan at Biju Patnaik University of Technology, Rourkela. This lecture is part of lecture series on Multimedia Applications course. It includes: Image, Compression, Standards, JPEG, Standard, Glance, Bitstream, Transform, Coding, Observations

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Lecture 20-21
Image Compression Standards
(Chapter 9 )
Contents
9.1 The JPEG Standard
9.1.1 Main Steps in JPEG Image
Compression
9.1.2 Four Commonly Used JPEG Modes
9.1.3 A Glance at the JPEG Bitstream
9.2 The JPEG2000 Standard
9.4 Bi-level Image Compression Standards
9.5 Further Exploration
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Lecture 20-

Image Compression Standards

(Chapter 9 )

Contents •

9.1 The JPEG Standard

  • 9.1.1 Main Steps in JPEG Image

Compression

  • 9.1.2 Four Commonly Used JPEG Modes – 9.1.3 A Glance at the JPEG Bitstream

9.2 The JPEG2000 Standard

9.4 Bi-level Image Compression Standards

9.5 Further Exploration

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9.1 The JPEG Standard

mally accepted as an international standard in 1992.by the “Joint Photographic Experts Group”. JPEG was for-JPEG is an image compression standard that was developed

JPEG is a

lossy

image compression method.

It employs a

transform coding

method using the DCT (

Discrete Cosine

Transform

An image is a function of

i

and

j

(or conventionally

x

and

y

in the

spatial domain

frequency response which is a functionThe 2D DCT is used as one step in JPEG in order to yield a

F

u, v

) in the

spatial

frequency domain

, indexed by two integers

u

and

v

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Observations for JPEG Image Compression

(cont’d)

Observation 2

Psychophysical experiments suggest that hu-

nents.frequency components than the loss of lower frequency compo-mans are much less likely to notice the loss of very high spatial

the high spatial frequency contents.the spatial redundancy can be reduced by largely reducing

Observation 3

: Visual acuity (accuracy in distinguishing closely

for color.spaced lines) is much greater for gray (“black and white”) than

chroma subsampling (4:2:0) is used in JPEG.

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Header

DPCM

RLC

Coding

DCT

Entropy

Quantization

Data

AC DC

Quantiz.

Tables

TablesCoding

Tables

YIQ or YUV

Zag

Zig

×

f (^) ( i, j

F

u, v

u, v

Fig. 9.1:

Block diagram for JPEG encoder.

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DCT on image blocks

Each image is divided into 8

×

8 blocks.

The 2D DCT is

applied to each block image

f

i, j

), with output being the

DCT coefficients

F

u, v

) for each block.

choppy (“blocky”) when a highfrom its neighboring context. This is why JPEG images lookUsing blocks, however, has the effect of isolating each block

compression ratio

is specified

by the user.

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Quantization

F ˆ

u, v

round

F

u, v

Q

u, v

F

u, v

) represents a DCT coefficient,

Q

u, v

) is a “quantiza-

tion matrix” entry, and ˆ

F

u, v

) represents the

quantized DCT

coefficients

which JPEG will use in the succeeding entropy

coding.

  • The

quantization

step

is

the

main

source

for

loss

in

JPEG

compression

The entries of

Q

u, v

) tend to have larger values towards the lower

right corner.

This aims to introduce more loss at the higher spatial

frequencies — a practice supported by Observations 1 and 2.

Table

and

show

the

default

Q

u, v

values

obtained

from

ratio while minimizing perceptual losses in JPEG images.psychophysical studies with the goal of maximizing the compression

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An 8

×

8 block from the Y image of ‘Lena’

f (^) ( i, j

F

u, v

Fig. 9.2:

JPEG compression for a smooth image block.

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u, v

u, v

f˜ (^) ( i, j

i, j

f (^) ( i, j

f˜ (^) ( i, j

Fig. 9.2 (cont’d):

JPEG compression for a smooth image block.

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u, v

u, v

f˜ 79 107 147 210 153 (^) ( i, j

i, j

f (^) ( i, j

f˜ (^) ( i, j

Fig. 9.3 (cont’d):

JPEG compression for a textured image block.

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  • Run-length Coding (RLC) on AC coefficients

RLC aims to turn the

F

u, v

) values into sets

{

#-zeros-to-

skip , next non-zero value

}

To make it most likely to hit a long run of zeros:

a

zig-zag

scan

is used to turn the 8

×

8 matrix ˆ

F

u, v

) into a

64-vector

Fig. 9.4:

Zig-Zag Scan in JPEG.

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Entropy Coding

ing step to gain a possible further compression.The DC and AC coefficients finally undergo an entropy cod-

cient, and AMPLITUDE contains the actual value.cates how many bits are needed for representing the coeffi-is represented by (SIZE, AMPLITUDE), where SIZE indi-Using DC as an example: each DPCM-coded DC coefficient

In the example we’re using, codes 150, 5,

8 will be

turned into

often.SIZE is Huffman coded since smaller SIZEs occur much more

AMPLITUDE is not Huffman coded,

its value can

change widely so Huffman coding has no appreciable benefit.

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Table 9.

Baseline entropy coding details — size category.

SIZE

AMPLITUDE

(Use 1’s-complement for negatives.)

So transmit/store:

{

Huffman(SIZE), Value

}

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secutiveNow, in a length-63 zig-zag, there can be up to 3 such con-

{

}

symbols

before a final one with a RUN-

zeros. To make it simple to indicate that there areLENGTH that finishes off the actual length of the run of

no more

non-zeros

, the symbol

{

}

is reserved as an “escape” sym-

bol denoted EOB which terminates the 8

×

8 block.

5. The SIZE is as before,

except that the possible range of

quantized AC coefficients is smaller.

For samples using

N

of bits, a numerical analysis shows that the (non-fractional part

the)

DCT

AC-coefficients

made

from

these

can

be

as

much

as

N

bits.

Baseline

coding

uses

8-bit

integer

samples in [

127] so amplitudes are in [

1023].

Table above.Therefore SIZE is from 1 to 10 as in the first 10 lines of the

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9.1.2 Four Commonly Used JPEG Modes

sumed in the discussions so far.Sequential Mode — the default JPEG mode, implicitly as-

Each graylevel image or

top-to-bottom scan.color image component is encoded in a single left-to-right,

Progressive Mode.

Hierarchical Mode.

JPEG-LS (Section 9.3).Lossless Mode — discussed in Chapter 7, to be replaced by

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