1. No category

JPEG and MPEG
Why compress?
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Limited storage space
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Transferring images over network
Esa Nuutinen
Kimmo Pajunen
Leo Rela
Pekka Repo
Juha Suikki
Lossy and lossless compression
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Lossless:
–
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What data will be lost?
●
all data will be kept
Lossy copression:
Human vision
–
not good at seeing small changes in color
–
good at seeing changes in brightness
–
some data will be lost in compression
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Details
–
better compression ratio
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Color information
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Edges
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Heavy compression causes disortion
JPEG
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Joint Photographic Experts group
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Defines lossy and lossless compression
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For photographs or artwork
–
2.7% of
original
not good for line drawings or cartoons
1% of
original
RGB to YUV
●
●
●
RGB images are compressed in JPEG by transforming
the images first into YUV and after that three color
components are compressed separately.
The chrominance components are often sub-sampled so
that a 2x2 block of the original pixels forms a new pixel
in sub-sampled image.
Human eye is weak in separating color differences in the
same luminance level.
RGB to YUV
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Lossless JPEG
• Lossless JPEG image is processed pixel by pixel in row-major
order.
• Value of the current pixel is predicted on the basis of neighboring
pixels that have been coded.
• When predicting pixel P(i,j), W=P(i,j-1), NW=P(i-1,j-1), N=P(i1,j) ja NE=P(i-1,j+1)
• Prediction functions are visible on the following table:
RGB => YUV
Y = 0,299*R+0,587*G+0,114*B
U = B-Y
V = R-Y
Y is luminance value
U and V are chromatic values
Mode:
Predictor:
Mode:
Predictor:
0
Null
4
N+W-NW
1
W
5
W+(N-NW)/2
2
N
6
N+(W-NW)/2
3
NW
7
N+W/2
Lossless JPEG Prediction Error
Lossless JPEG Example
Huffman coding of the prediction errors.
Category:
Codeword:
Difference:
Codeword:
0
00
0
-
1
010
-1, 1
0, 1
2
011
-3, -2, 2, 3
00, 01, 10, 11
3
100
-7,...-4, 4...7
000,...011, 100,... 111
4
101
-15,...-8, 8...15
0000,...0111, 1000,... 1111
5
110
-31,...-16, 16...31
:
6
1110
-63,...-32, 32...63
:
7
11110
-127,...-64, 64...127
:
8
111110
-255,...-128, 128...255
:
• In the following example, prediction mode 1 has been
used in the pixel sequence ( 10, 12, 7, 8, 8, 12 ).
Pixel:
10
12
10
7
8
8
12
Prediction error:
+10
+2
-2
-3
+1
0
+4
4
2
2
2
1
0
3
101
011
011
011
010
00
100
1010
10
10
00
1
Category:
Bit sequence:
100
JPEG - Huffman Coding
●
●
●
The Idea in Huffman Coding is that some
characters etc. are more common than others.
Common characters are coded using less bits than
rare characters.
This way data will go in smaller space.
Basic lossy JPEG encoder
●
JPEG - Huffman Coding
Encoder Block Diagram
Header
Compressed data
Image Data
8x8 Blocks
DCT
Quantizer
JPEG - DCT
• DCT: ”Discrete Cosine Transform”
• Each 8x8 block will be transformed in to
frequency domain.
Huffman Encoder
DCT
Quantization Tables
Huffman Tables
F (u, v ) =
CuCv 7 7
⎡ ( 2i + 1) ⋅ uπ ⎤
⎡ ( 2 j + 1) ⋅ vπ ⎤
⋅ cos ⎢
cos ⎢
∑∑
⎥
⎥⎦ ⋅ f (i, j )
4 i =0 j =0
16
16
⎣
⎦
⎣
⎧ 1
⎪
Cu, Cv = ⎨ 2
⎪⎩ 1
u, v = 0
otherwise
Basic lossy JPEG decoder
DCT
Header
Reconstructed Image
Data in 8x8 blocks
Compressed
data
Huffman Decoder
Dequantizer
Huffman Tables
•If there is only one shade of gray
in 8x8 picture block, the only
weighted value will be in the top
left corner.
IDCT
Quantization tables
7
7
f ( x, y ) = ∑∑
i =0 j =0
IDCT
•8x8 matrix. Lower frequencies
are in the top left on higher on
bottom right.
⎧ 1
⎪
Cu, Cv = ⎨ 2
⎪⎩ 1
CuCv
⎡ ( 2 y + 1) ⋅ vπ ⎤
⎡ ( 2 x + 1) ⋅ uπ ⎤
cos ⎢
⎥⎦ ⋅ F (u, v )
⎥⎦ ⋅ cos ⎢⎣
16
16
4
⎣
u, v = 0
otherwise
JPEG Quantization
⎛ F [u, v ] ⎞
F ' [u, v ] = round ⎜⎜
⎟⎟
⎝ q[u, v ] ⎠
• The idea is to reduce the number of bits per sample.
• Example:
45 = 101101 ( 6 bits )
q[u,v]=4 -> 45/4 = 11 = 1011 (4 bits )
• This is the main reason for data loss in lossy JPEG.
• In JPEG the quantization factor is not uniform within the
block.
JPEG – Quantization Table
●
●
●
●
The idea in these tables is that more bits are allocated for
the low frequency components than to high frequency
components.
Quantization tables are stored in JPEG file header.
The Luminance Quantization table is used for grey scale
images and Y color component (in YUV color space).
The Chrominance Quantization table is used for U and V
color components.
The bit rate can be adjusted by scaling the basic
quantization tables either up ( for low bit rate ) or down
(for higher bit rates.)
JPEG – Quantization Tables
JPEG – Quantization Tables
The Luminance Quantization Table
The Chrominance Quantization Table
16
11
10
16
24
40
51
61
17
18
24
47
99
99
99
99
12
12
14
19
26
58
60
55
18
21
26
66
99
99
99
99
14
13
16
24
40
57
69
56
24
26
56
99
99
99
99
99
14
17
22
29
51
87
80
62
47
66
99
99
99
99
99
99
18
22
37
56
68
109
103
77
99
99
99
99
99
99
99
99
24
35
55
64
81
104
113
92
99
99
99
99
99
99
99
99
49
64
78
87
103
121
120
101
99
99
99
99
99
99
99
99
72
92
95
98
112
100
103
99
99
99
99
99
99
99
99
99
JPEG – Zig-zag scan
●
The Idea is to group low frequency coefficients
on top of the vector. Maps 8x8 to a 1x64 vector.
JPEG
●
●
After zig-zag long sequences of zero value
coefficients are coded by their number (the length
of the run).
Huffman coding is then applied to the non-zero
coefficients.
JPEG Example
JPEG Example
15
DCT
JPEG Example
235,6
-1
round(
=
)
16
11
15
0
JPEG - Example
16*15=240
•There are a lot of zeros in the
bottom right corner of matrix. So
it is quite easy to compress the
image data even more. This can
be done by using methods
described earlier.
0
JPEG Example
References
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IDCT
●
•If the original and the decompressed
block are compared, it can be noticed
that there are some small differences.
●
●
Some high frequency components are discarded. What
happens (in practice)?
http://www.it.lut.fi/opetus/9899/1970/seminars/05/node11.html
http://www.it.lut.fi/opetus/9798/1588/references/Franti.ps
http://rnvs.informatik.tuchemnitz.de/~jan/MPEG/HTML/mpeg_tech.html
Esa Kerttula, Luentomoniste, kevät 2003 Telematiikka
(1630) Osa I. 27.4.2003
Exaggerated compression: How 8x8 blocks
are being approximated.
8x8
block
Original picture with all 64 coefficients (left)
15 coefficients, the produced error is small (middle)
6 coefficients, the error remarkable (right)
So,
less than ¼ of the 64 values are needed to achieve a good
approximation of the original image
[1]
Same picture before and after compression. In the left picture
8x8 blocks are visible.
(Exaggerated = liioiteltu)
Compressed picture and trails from the DCT
The eye example
Original
uncompressed bmp file,
640x480
Jpeg
Jpeg
~ 20 kB
~ 9 kB
~1MB
The difference (error) between pictures can only hardly be seen
DCT 8x8 basis vectors
What kind of pictures can be compressed
efficiently?
JPEG: 2 used colors, line
drawing,
JPEG: 24 bit colors, photo,
hard edges, single color
textures
6 435 bytes
14 568 bytes
smooth edges and textures
Present JPEG extensions
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●
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Progressive mode:
Lossless JPEG (LJPEG)
-1995
–
Supports real-time transmission of images
-Does not use DCT
–
Coefficients are sent in multiple scans of the original image
-Codes the difference between each pixel and predicted value for the pixel.
–
Low quality preview can be sent first and then comes rest ”incremental” images.
-Eight different predictor functions are used
Variable quantization
–
Allows quantization table to be altered for different parts of the image.
–
Some parts of the image can be compressed with higher quality.
-Huffman coding
-Exact losslessness is not guaranteed (depends on encoder and decoder
implementations)
Hierarchical mode:
–
Same image with multiple resolutions
–
Higher resolution images are represented as differences from the next smaller
resolution image.
[4]
[2, 4]
JPEG 2000
-Wavelet compression
-About 20% better compression than present JPEG
References
1
The Scientist and Engineer's Guide to Digital Signal Processing.
California Technical Publishing, 1997. ISBN 0-9660176-3-3.
2
Seppo Virtanen: An introduction to JPEG. Course material –
Multimedia algorithms spring 2000. Laboratory of electronics and
information technology, University of Turku
3
Jpeg website, http://www.jpeg.org/ [25th Feb 2004]
4
The JPEG tutorial,
-Uses progressive mode
-Not very supported on Feb 2004
[3]
http://dynamo.ecn.purdue.edu/~ace/jpeg-tut/jpegtut1.html
[25th Feb 2004]
MPEG
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Motion Picture Expert Group
●
Basic ideas:
●
–
sub sampling
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removing redundancy inside frames
–
removing redundancy between frames
I-frames
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All information needed to construct the frame
is present
●
Compression similar to JPEG
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Can be used to construct other frames
MPEG video feed consist of three types of frames
–
I-frames
–
P-frames
–
B-frames
P-frames
●
●
●
P-frame is predicted from previous I- or P-frame
–
The frame is divided and processed in macroblocks
(16x16 pixels)
–
Macro blocks in the current frame are compared to
content of the previous frame
–
●
P-frames
Goal is to find similarities
●
If similar block is found:
–
Difference in position is saved in a movement vector (mv)
●
The block from the previous frame is subtracted
from the block in the current frame to get
difference block
mv and the difference block are sent then to the
receiver
If similar blocks not found, whole block is sent
(as in I-frame)
If blocks are exactly the same, nothing is sent
Motion Prediction
B-frames
frame 1 (I-frame)
●
frame 2 (P-frame)
mv
●
●
Processed like P-frames, but information
from both previous and next I- or P-frame
can be used (Bi-Directional prediction)
Offers usually best compression ratio
B-frames are never used as source of
information
difference block
GOP
MPEG-1 and MPEG-2
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Video is processed in Group Of Pictures
●
Begins with I-frame ( length usually 10-15 frames )
–
first generation
●
Usual sequence is IBBPBBPBB…
–
VHS quality, 1.5 Mbit/s
–
made digital video possible
●
●
MPEG-1
MPEG-2
–
generic coding of video and audio
–
greater quality, up to 4 Mbit/s
–
compression ratio between 50:1 and 25:1
MPEG-3 and MPEG-4
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MPEG-3
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●
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MPEG-7
–
HDTV (High Definition TV)
–
DDT (Description Definition Language)
–
obsolete
–
enables searchable content in video clips
MPEG-4
–
encoding/decoding of audio-visual objects
–
body animation, games, high quality virtual
environments
–
speech and video synthesis, fractal geometry, artificial
intelligence
MPEG References
●
MPEG-7 and MPEG-21
http://www.mpeg.org
http://www.disctronics.co.uk/technology/video/vi
deo_mpeg.htm
Sikora, T.; MPEG digital video-coding standards
Signal Processing Magazine, IEEE , Volume: 14 ,
Issue: 5 , Sept. 1997. Pages:82 - 100
●
MPEG-21
–
framework for content creation/transfer
–
based on digital items
–
multiple standards may be used within the framework