CM613 Multimedia storage and retrieval Lossy Compression

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CM613 Multimedia storage and retrievalLossy
Compression

• D.Miller

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What is lossy compression

• Trade-offs
• A design trade-off is necessary when there is a
  contradiction in requirements
• Examples
• Cost versus quality
• Cost versus performance
• Usability for occasional or inexperienced users
  versus power or flexibility for expert users.
• Size or download time versus resolution of image
• Decisions are made using criteria based on
  sufficiency for purpose.

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What is lossy compression
In lossy compression, as opposed to Lossless
compression data is compressed then
decompressing so that the retrieved
data, although different from the original, is
still "close enough" to be useful in some
way. Depending on the design of the format
lossy data compression often suffers
from generation loss, that is compressing and
decompressing multiple times will do more damage
to the data than doing it once.
http//en.wikipedia.org/wiki/Lossy_compression
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What is lossy compression

• There are two basic lossy compression schemes
• Transform
• In lossy transform codecs,
• samples of picture or sound are taken,
• chopped into small segments,
• transformed into a new basis space,
• and quantized.
• The resulting quantized values are then given
  variable length codes, depending on their
  frequency of occurrence (entropy coding).

http//en.wikipedia.org/wiki/Lossy_compression
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What is lossy compression

• The second type of scheme is
• Predictive
• In lossy predictive codecs, previous and/or
  subsequent decoded data is used to predict the
  current sound sample or image frame.
• The error between the predicted data and the real
  data, together with any extra information needed
  to reproduce the prediction, is then quantized
  and coded.
• In some systems transform and predictive
  techniques are combined, with transform codecs
  being used to compress the error signals
  generated by the predictive stage.

http//en.wikipedia.org/wiki/Lossy_compression
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How it works human perception perspective
JPEG exploits the characteristics of human
vision, eliminating or reducing data to which the
eye is less sensitive. JPEG works well on
grayscale and color images, especially on
photographs, but it is not intended for two-tone
images. http//www.planetanalog.com/features/OEG2
0030122S0063
Lena Image, Highly Compressed (96 less
information, 0.56KB)
Lena Image, Compressed (85 less information,
1.8KB)
Original Lena Image (12KB size)
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How it works technical process
Example JPEG
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JPEG (Joint Photographic Experts Group)
JPEG (pronounced jay-peg) is a most commonly used
standard method of lossy compression for
photographic images. JPEG itself specifies only
how an image is transformed into a stream of
bytes, but not how those bytes are encapsulated
in any particular storage medium. A further
standard, created by the Independent JPEG Group,
called JFIF (JPEG File Interchange Format)
specifies how to produce a file suitable for
computer storage and transmission from a JPEG
stream. In common usage, when one speaks of a
"JPEG file" one generally means a JFIF file, or
sometimes an Exif JPEG file. JPEG/JFIF is the
format most used for storing and transmitting
photographs on the web.. It is not as well suited
for line drawings and other textual or iconic
graphics because its compression method performs
badly on these types of images
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Baseline JPEG compression
http//www.planetanalog.com/features/OEG20030122S0
063
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Y luminance Cr, Cb chrominance
YCbCb colour space is based on YUV colour
space YUV signals are created from an original
RGB (red, green and blue) source. The weighted
values of R, G and B are added together to
produce a single Y (lumsignal, representing the
overall brightness, or luminance, of that spot.
The U signal is then created by subtracting the Y
from the blue signal of the original RGB, and
then scaling and V by subtracting the Y from the
red, and then scaling by a different factor. This
can be accomplished easily with analog
circuitry. The following equations can be used to
derive Y, U and V from R, G and B Y 0.299R
0.587G 0.114B U 0.492(B - Y) - 0.147R -
0.289G 0.436B V 0.877(R - Y) 0.615R - 0.515G
- 0.100B
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Discrete cosine transform
DCT transforms the image from the spatial domain
into the frequency domain Next, each component
(Y, Cb, Cr) of the image is "tiled" into sections
of eight by eight pixels each, then each tile is
converted to frequency space using a
two-dimensional forward discrete cosine transform
(DCT, type II).
The 64 DCT basis functions
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the coefficient in the output DCT matrix at
(2,1) corresponds to the strength of the
correlation between the basis function at (2,1)
and the entire 8x8 input image block. The
coefficients corresponding to high-frequency
details are located to the right and bottom of
the DCT block, and it is precisely these weights
which we try to nullify -- the more zeroes in the
8x8 DCT block, the higher the compression that is
achieved. In the Quantization step below,
we'll discuss how to maximize the number of
zeroes in the matrix.
http//www.planetanalog.com/features/OEG20030122S0
063
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Quantization
This is the main lossy operation in the whole
process. After the DCT has been performed on
the 8x8 image block, the results are quantized in
order to achieve large gains in compression
ratio. Quantization refers to the process of
representing the actual coefficient values as one
of a set of predetermined allowable values, so
that the overall data can be encoded in fewer
bits (because the allowable values are a small
fraction of all possible values).
The aim is to greatly reduce the amount of
information in the high frequency components.
This is done by simply dividing each component
in the frequency domain by a constant for that
component, and then rounding to the nearest
integer. As a result of this, it is typically
the case that many of the higher frequency
components are rounded to zero, and many of the
rest become small positive or negative numbers.
Example of a quantizing matrix
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Quantization
Quantization is the key irreversible step in the
jpeg process. Jpeg Quality Settings Typically
the only thing that the user can control in Jpeg
compression is the quality setting (and rarely
the chroma sub-sampling). The value chosen is
used in the quantization stage, where less common
values are discarded by using tables tuned to
visual perception. This reduces the amount of
information while preserving the perceived
quality. http//www.photo.net/learn/jpeg/percep

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Zig-zag sorting
The quantized data needs to be in an efficient
format for encoding. The quantized coefficients
have a greater chance of being zero as the
horizontal and vertical frequency values
increase. To exploit this behavior, we can
rearrange the coefficients into a one-dimensional
array sorted from the DC value to the
highest-order spatial frequency coefficient
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The first element in each 64x1 array represents
the DC coefficient from the DCT matrix, and the
remaining 63 coefficients represent the AC
components. These two types of information are
different enough to warrant separating them and
applying different methods of coding to achieve
optimal compression efficiency. All of the DC
coefficients (one from each DCT output block)
must be grouped together in a separate list. At
this point, the DC coefficients will be encoded
as a group and each set of AC values will be
encoded separately.
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Coding the DC Coefficients
The DC components represent the intensity of each
8x8 pixel block. Because of this, significant
correlation exists between adjacent blocks. So,
while the DC coefficient of any given input array
is fairly unpredictable by itself, real images
usually do not vary widely in a localized area.
As such, the previous DC value can be used to
predict the current DC coefficient value. By
using a differential prediction model (DPCM), we
can increase the probability that the value we
encode will be small, and thus reduce the number
of bits in the compressed image. To obtain the
coding value we simply subtract the DC
coefficient of the previously processed 8x8 pixel
block from the DC coefficient of the current
block. This value is called the "DPCM
difference". Once this value is calculated, it
is compared to a table to identify the symbol
group to which it belongs (based on its
magnitude), and it is then encoded appropriately
using an entropy encoding scheme such as Huffman
coding.
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Coding the AC Coefficients (Run-Length Coding)
Because the values of the AC coefficients tend
towards zero after the quantization step, these
coefficients are run-length encoded. The
concept of run-length encoding is a
straightforward principle. In real image
sequences, pixels of the same value can always be
represented as individual bytes, but it doesn't
make sense to send the same value over and over
again. For example, we have seen that the
quantized output of the DCT blocks produces many
zero-value bytes. The zig-zag ordering helps
produce these zeros in groups at the end of each
sequence. Instead of coding each zero
individually, we simply encode the number of
zeros in a given 'run.' This run-length coded
information is then variable-length coded (VLC),
usually using Huffman codes.
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Entropy encoder
This is a final lossless compression performed on
the quantized DCT coefficients to increase the
overall compression ratio achieved.
Entropy encoding is a compression technique that
uses a series of bit codes to represent a set of
possible symbols.
Fixed length codes are most often applied in
systems where each of the symbols occurs with
equal probability.
Example of a fixed length code
In reality, most symbols do not occur with equal
probability. In these cases, we can take
advantage of this fact and reduce the average
number of bits used to compress the sequence.
The length of the code that is used for each
symbol can be varied based on the probability of
the symbol's occurrence. By encoding the most
common symbols with shorter bit sequences and the
less frequently used symbols with longer bit
sequences, we can easily improve on the average
number of bits used to encode a sequence.
Example of entropy encoding with weighted symbol
probabilities.
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JPEG File Interchange Format (JFIF)
The encoded data is written into the JPEG File
Interchange Format (JFIF), which, as the name
suggests, is a simplified format allowing
JPEG-compressed images to be shared across
multiple platforms and applications. JFIF
includes embedded image and coding parameters,
framed by appropriate header information.
Specifically, aside from the encoded data, a
JFIF file must store all coding and quantization
tables that are necessary for the JPEG decoder to
do its job properly.
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So now we can all grow beards!
Quality factor 20
Quality factor 5
Quality factor 3
http//www.imaging.org/resources/jpegtutorial/jpgi
mag1.cfm
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Sources
http//www.answers.com/topic/ycbcr-sampling http
//www.answers.com/main/ntquery?method4dsid1512
dekeyYUVgwp8curtab1512_1linktextYUV http
//www.imaging.org/resources/jpegtutorial/jpgimag1.
cfm http//www.photo.net/learn/jpeg/percep http
//www.planetanalog.com/features/OEG20030122S0063
http//en.wikipedia.org/wiki/Lossy_compression
end