Multimedia Compression ( Lossy Compression)

7/31/2019 Multimedia Compression ( Lossy Compression)

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-- Muktinath Vishwakarma7th Semester (CSE)

RGCER, NAGPUR

Thanks to:

Prof. H.R. Turkar Sir.

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Data compression is the art

of reducing the number ofbits needed to store ortransmit data.

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Compression reduces the size of a

file: To save space when storing it.

To save time when transmitting it.

Most files have lots of redundancy.

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1: Lossless Compression

-- Shannon-Fano Algorithm

-- Huffman Coding

-- LZW Compression

2: Lossy Compression

-- Transform Coding.

1: DCT (Discrete Cosine Transform)

2: KCT (Karhunen-Laeve Transform)

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-- Distortion Measures

-- The Rate Distortion theory

-- Quantization

1: Uniform Scalar Quantization

2: Non Uniform Scalar Quantization

-- Transform Coding

1: DCT

2: KCT

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Mathematical Quantity

Specify how close an approximation is to its original,

using some distortion criteria.

Where, Row is Mean square error (MSE), Xn is inputdata sequence, Yn is reconstructed data sequence, N is

length of the data sequence.

SNR (O/MSE), PSNR(Peak/MSE)

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Always involves a tradeoff between rate and distortion.

Rate is the average number of bits required to represent

each source symbol.

The tradeoff between rate and distortion is represented

in the form of rate distortion function R(D).

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Heart of Any Lossy scheme

Aim to reduce number ofdistinct

values to a much smaller set.

1: Uniform Scalar Quantization

2: Non Uniform Scalar Quantization

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Partition the domain of inputs values into equalspaced intervals, except possibly at the two outer

intervals.

The endpoint of partition intervals are called the

quantizer's decision boundaries. Output/value corresponding to each interval is taken

to be the mid point of the intervals.

length of each interval step size (delta triangle)

It is of two types.

1: Midtread ( 0, Odd no of o/p level )

2: Midrise( (0), Even no of o/p level )

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If input source is not uniformly distributed.

It may be inefficient.

Increasing the number of decision levels within the

regions where the source is densely distributed can

effectively lower granular distortion.

In addition, Without having to increase the total

number of decisions levels, we can enlarge the

region in which the source is sparsely distributed.

Such Non Uniform quantizers thus have non

uniformly defined decision boundaries.

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Coding Vectors is more efficient than coding scalar

We need to group block of consecutive samples fromsource input into vectors.

The rationale behind transform coding:

IfY is the result of a linear transform T of the input vectorX in such a way that the components ofY are much lesscorrelated, then Y can be coded more efficiently than X.

If most information is accurately described by the first fewcomponents of a transformed vector, then the remainingcomponents can be coarsely quantized, or even set to zero,with little signal Distortion.

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A widely used transform coding technique, is able to perform

decorrellation of the input signal in a data-independent manner.Because of this it has gain tremendous popularity.

Definition of DCT:

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Fundamental of Multimedia, Ze-Nian Li, Mark S.

Drew.

http://mattmahoney.net/dc/dce.html#Section_6

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

http://www.ics.uci.edu/~dan/pubs/DataCompression.html

http://www.cs.cmu.edu/~guyb/realworld/compression

.pdf

http://www.data-compression.com/index.shtml

http://www.cs.princeton.edu/~rs/AlgsDS07/20Compres

sion.pdf

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http://mattmahoney.net/dc/dce.htmlhttp://en.wikipedia.org/wiki/Data_compressionhttp://www.ics.uci.edu/~dan/pubs/DataCompression.htmlhttp://www.ics.uci.edu/~dan/pubs/DataCompression.htmlhttp://www.cs.cmu.edu/~guyb/realworld/compression.pdfhttp://www.cs.cmu.edu/~guyb/realworld/compression.pdfhttp://www.data-compression.com/index.shtmlhttp://www.cs.princeton.edu/~rs/AlgsDS07/20Compression.pdfhttp://www.cs.princeton.edu/~rs/AlgsDS07/20Compression.pdfhttp://www.cs.princeton.edu/~rs/AlgsDS07/20Compression.pdfhttp://www.cs.princeton.edu/~rs/AlgsDS07/20Compression.pdfhttp://www.data-compression.com/index.shtmlhttp://www.data-compression.com/index.shtmlhttp://www.data-compression.com/index.shtmlhttp://www.data-compression.com/index.shtmlhttp://www.cs.cmu.edu/~guyb/realworld/compression.pdfhttp://www.cs.cmu.edu/~guyb/realworld/compression.pdfhttp://www.cs.cmu.edu/~guyb/realworld/compression.pdfhttp://www.ics.uci.edu/~dan/pubs/DataCompression.htmlhttp://www.ics.uci.edu/~dan/pubs/DataCompression.htmlhttp://www.ics.uci.edu/~dan/pubs/DataCompression.htmlhttp://en.wikipedia.org/wiki/Data_compressionhttp://en.wikipedia.org/wiki/Data_compressionhttp://mattmahoney.net/dc/dce.htmlhttp://mattmahoney.net/dc/dce.html


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Multimedia Compression ( Lossy Compression)

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