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CODING AND COMPRESSION

PRESENTED BY: PING CHEN

CECS401 UMC

DATE: April, 29 2000

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Coding and Compression

IntroductionLossless Data Compression

Runlength, Huffman, Dictionary compression

Audio ADPCM, LPC, CELP

Image hierarchical coding, subband coding MPEG

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Introduction

A key problem with multimedia is the huge quantities of data that result from raw digitized data of audio, image or video source.

The main goal for coding and compression is to alleviate the storage, processing and transmission costs for these data.

There are a variety of compression techniques commonly used in the Internet and other system.

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Introduction

The components of a system are capturing, transforming, coding and transmitting.

Sample Transform Coding

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Introduction

Sampling --- Analog to Digital Conversion. An input signal is converted from some continuously

varying physical value(e.g. pressure in air, or frequency or wavelength of light) into a continuously electrical signal by some electro-mechanical device.

This continuously varying electrical signal can then be converted to a sequence of digital values, called samples, by some analog to digital conversion circuit.

Two factors determine the accuracy of the sample with the original continuous signal:

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Introduction

The maximum rate at which we sample.Based on Nyquist’s theorem, the digital sampling

rate must be twice of the highest frequency in continuous signal.

The number of bits used in each sample. (known as the quantization level.)

however, it is often not necessary to capture all frequencies in the original signal.

For example, voice is comprehensible with a much smaller range of frequencies that we can actually hear.

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Introduction

The goal of transform is to decorrelate the original signal, and this decorrelation results in the signal energy being redistributed among only a small set of transform coefficients.

The original data can be transformed in a number of ways to make it easier to apply certain compression techniques.

The most common transform in current techniques are the Discrete Cosine Transform and wavelet transform.

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Lossless Data Compression

Lossless means the reconstructed image doesn’t lose any information according to the original one.

There is a huge range of lossless data compression techniques.

The common techniques used are: runlength encoding Huffman coding dictionary techniques

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Lossless Data Compression

Runlength compression Removing repetitions of values and replacing

them with a counter and single value. Fairly simple to implement. Its performance depends heavily on the input

data statistics. The more successive value it has, the more space we can compress.

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Lossless Data Compression

Huffman compression Use more less bits to represent the most

frequently occurring characters/codeword values, and more bits for the less commonly occurring once.

It is the most widespread way of replacing a set of fixed size code words with an optimal set of different sized code words, based on the statistics of the input data.

Sender and receiver must share the same codebook which lists the codes and their compressed representation.

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Lossless Data Compression

Dictionary compression Look at the data as it arrives and form a

dictionary. when new input comes, it look up the dictionary. If the new input existed, the dictionary position can be transmitted; if not found, it is added to the dictionary in a new position, and the new position and string is sent out.

Meanwhile, the dictionary is constructed at the receiver dynamically, so that there is no need to carry out statistics or share a table separately.

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Audio

The input audio signal from a microphone is passed through several stages: firstly, a band pass filter is applied eliminating

frequencies in the signal that we are not interested in.

then the signal is sampled, converting the analog signal into a sequence of values.

This is then quantised, or mapped into one of a set of fixed value.

These values are then coded for storage or transmission.

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Audio

Some techniques for audio compression: ADPCM LPC CELP

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Audio

ADPCM -- Adaptive Differential Pulse Code Modulation ADPCM allows for the compression of PCM

encoded input whose power varies with time. Feedback of a reconstructed version of the

input signal is subtracted from the actual input signal, which is quantised to give a 4 bits output value.

This compression gives a 32 kbit/s output rate.

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Audio

Qunatizer

Predictor

Coder

ChannelEm*Em

Original

Xm

Xm*Xm'

+

+

+-

Transmitter

Predictor

Reconstructed

Xm*

Channel

Xm'

++

Receiver

Em*

Decoder

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Audio

LPC -- Linear Predictive Coding The encoder fits speech to a simple, analytic

model of the vocal tract. Only the parameters describing the best-fit model is transmitted to the decoder.

An LPC decoder uses those parameters to generate synthetic speech that is usually very similar to the original.

LPC is used to compress audio at 16 Kbit/s and below.

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Audio -- CELP

CELP -- Code Excited Linear Predictor CELP does the same LPC modeling but then

computers the errors between the original speech and the synthetic model and transmits both model parameters and a very compressed representation of the errors.

The result of CELP is a much higher quality speech at low data rate.

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Image

Hierarchical Coding based on the idea that coding will be in the

form of quality hierarchy where the lowest layer of hierarchy contains the minimum information for intelligibility.

It is ideal for transmission over packet switched network, low level packets can be filtered out wherever a low bandwidth link is encountered and still delivering a better quality to sites.

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Image

Subband Coding an example of an encoding algorithm that can

map onto hierarchical coding. based on the fact that the low spatial

frequencies components of a picture do carry most of the information within the picture.

The picture can thus be divided into its spatial frequencies components.

Allocate each subband to one of the hierarchy layers.