CODING AND COMPRESSION

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CODING AND COMPRESSION

• PRESENTED BY PING CHEN
• CECS401 UMC
• DATE April, 29 2000

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Coding and Compression

• Introduction
• Lossless 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.

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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 Nyquists 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 doesnt
  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
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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.