CODING AND COMPRESSION

1. CODING AND COMPRESSION PRESENTED BY: PING CHEN CECS401 UMC DATE: April, 29 2000

2. Coding and Compression • Introduction • Lossless Data Compression • Runlength, Huffman, Dictionary compression • Audio • ADPCM, LPC, CELP • Image • hierarchical coding, subband coding • MPEG

3. 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.

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

5. 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:

6. 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.

7. 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 datacan 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.

8. 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

9. 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.

10. 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.

11. 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.

12. 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.

13. Audio • Some techniques for audio compression: • ADPCM • LPC • CELP

14. 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.

15. Audio

16. 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.

17. 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.

18. 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.

19. 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.