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

2. 2 Coding and Compression zIntroduction zLossless Data Compression yRunlength, Huffman, Dictionary compression zAudio yADPCM, LPC, CELP zImage yhierarchical coding, subband coding yMPEG

3. 3 Introduction zA key problem with multimedia is the huge quantities of data that result from raw digitized data of audio, image or video source. zThe main goal for coding and compression is to alleviate the storage, processing and transmission costs for these data. zThere are a variety of compression techniques commonly used in the Internet and other system.

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

5. 5 Introduction zSampling --- Analog to Digital Conversion. yAn 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. yThis continuously varying electrical signal can then be converted to a sequence of digital values, called samples, by some analog to digital conversion circuit. zTwo factors determine the accuracy of the sample with the original continuous signal:

6. 6 Introduction yThe maximum rate at which we sample. xBased on Nyquist’s theorem, the digital sampling rate must be twice of the highest frequency in continuous signal. yThe number of bits used in each sample. (known as the quantization level.) yhowever, it is often not necessary to capture all frequencies in the original signal. xFor example, voice is comprehensible with a much smaller range of frequencies that we can actually hear.

7. 7 Introduction zThe 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. zThe original data can be transformed in a number of ways to make it easier to apply certain compression techniques. zThe most common transform in current techniques are the Discrete Cosine Transform and wavelet transform.

8. 8 Lossless Data Compression zLossless means the reconstructed image doesn’t lose any information according to the original one. zThere is a huge range of lossless data compression techniques. zThe common techniques used are: yrunlength encoding yHuffman coding ydictionary techniques

9. 9 Lossless Data Compression zRunlength compression yRemoving repetitions of values and replacing them with a counter and single value. yFairly simple to implement. yIts performance depends heavily on the input data statistics. The more successive value it has, the more space we can compress.

10. 10 Lossless Data Compression zHuffman compression yUse more less bits to represent the most frequently occurring characters/codeword values, and more bits for the less commonly occurring once. yIt 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. ySender and receiver must share the same codebook which lists the codes and their compressed representation.

11. 11 Lossless Data Compression zDictionary compression yLook 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. yMeanwhile, the dictionary is constructed at the receiver dynamically, so that there is no need to carry out statistics or share a table separately.

12. 12 Audio zThe input audio signal from a microphone is passed through several stages: yfirstly, a band pass filter is applied eliminating frequencies in the signal that we are not interested in. ythen the signal is sampled, converting the analog signal into a sequence of values. yThis is then quantised, or mapped into one of a set of fixed value. yThese values are then coded for storage or transmission.

13. 13 Audio zSome techniques for audio compression: yADPCM yLPC yCELP

14. 14 Audio zADPCM -- Adaptive Differential Pulse Code Modulation yADPCM allows for the compression of PCM encoded input whose power varies with time. yFeedback 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. yThis compression gives a 32 kbit/s output rate.

15. 15 Audio

16. 16 Audio zLPC -- Linear Predictive Coding yThe 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. yAn LPC decoder uses those parameters to generate synthetic speech that is usually very similar to the original. yLPC is used to compress audio at 16 Kbit/s and below.

17. 17 Audio -- CELP zCELP -- Code Excited Linear Predictor yCELP 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. yThe result of CELP is a much higher quality speech at low data rate.

18. 18 Image zHierarchical Coding ybased 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. yIt 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. 19 Image zSubband Coding yan example of an encoding algorithm that can map onto hierarchical coding. ybased on the fact that the low spatial frequencies components of a picture do carry most of the information within the picture. yThe picture can thus be divided into its spatial frequencies components. yAllocate each subband to one of the hierarchy layers.