MULTIMEDIA SYSTEMS

Transcription

1 1 Department of Computer Engineering, Faculty of Engineering King Mongkut s Institute of Technology Ladkrabang MULTIMEDIA SYSTEMS Pk Pakorn Watanachaturaporn, Wt ht Ph.D. PhD pakorn@live.kmitl.ac.th, pwatanac@gmail.com Chapter 1 Digital Data Representation and Communication 2

2 Analog to Digital Conversion Analog versus Discrete Phenomena Analog phenomena are continuous No clear separation between one point and the next Discrete phenomena are clearly separated There is a point (in space or time) Then, there is a neighboring point There is nothing between the two Analog-to-digital conversion Converting the continuous o phenomena of images, sound, and motion into a discrete representation 3 Analog to Digital Conversion (Cont d) Analog Media Run smoothly and continuously More precise and give better quality in photograph, music, and videos More vulnerable to noise than is digital Lose some of its quality in transmission 4

3 Analog to Digital Conversion (Cont d) Digital Media Communicated entirely as a sequence of 0s and 1s Error-correcting strategies can be employed to ensure that the data is received and interpreted correctly Be communicated more compactly than analog Compression algorithms significantly reduce the amount of data without sacrificing quality 5 Image and Sound Data Represented as Functions and Waveforms Two primary media in digital media are images and sound Video are produced from the combination of images and sound Sound is a one-dimensional function a function with one variable as input y = f ( x) where x is time and y is the air pressure amplitude 6

4 7 Image and Sound Data Represented as Functions and Waveforms (Cont d) Sound is a mechanical wave Results from the motion of particles through a transmission medium Cannot be transmitted through a vacuum A longitudinal wave The motion of individual particles is parallel to the direction in which energy is being transported. 8

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6 Image and Sound Data Represented as Functions and Waveforms (Cont d) Awave is periodic if it repeats a pattern over time The pattern that is repeated constitutes one cycle of the wave A wavelength is the length (in distance) of one complete cycle The frequency of a wave is the number of times a cycle cle repeats per unit time cycles cles per second, or Hertz (Hz) 11 Image and Sound Data Represented as Functions and Waveforms (Cont d) Let T be the period and f be the frequency of a sinusoidal id wave T = 1 Let f be the frequency of a sine wave measured in Hz. Let ω be the equivalent angular frequency measured in radians/s. ω = f 2π f 12

7 13 Image and Sound Data Represented as Functions and Waveforms (Cont d) A complex waveform can be broken down mathematically ti into its frequency components The Fourier transform / Fourier Analysis Any periodic signal can be decomposed into an infinite sum of sinusoidal waveforms The simple sinusoidal id waves are called the frequency components of the more complex wave 14

8 Image and Sound Data Represented as Functions and Waveforms (Cont d) Sinusoidal waveforms can be used to represent changing color amplitudes in a digital it image Each point in the picture can be represented by a number its grayscale value Grayscale values range from 0 to 255 and correspond to shades of gray, black, and white 15 16

9 Sampling and Aliasing 17 Sampling and Aliasing (Cont d) A digital camera detects the color of the object at each sample block and records the information in a pixel short for picture element What if one sampled only every other block? Undersampling sampling rate did not keep up with the rate of change of the pattern in the image Aliasing Arises from undersampling An image does not match the original source Blur False pattern 18

10 19 Sampling and Aliasing (Cont d) Aliasing a situation where one thing takes the form or identity of another In digital images a lack of clarity or a pattern in the digital image that does not exist in the original In text jagged edges on letters that ought to be smooth In digital audio sound frequencies that did not exist in the eoriginal 20

11 Sampling and Aliasing (Cont d) Nyquist theorem Specified the sampling rate needed for a given spatial or temporal frequency To guarantee that no aliasing will occur, a sampling rate that is greater than twice the frequency of signal being sampled 21 Quantization, Quantization Error, and Signal-to-Noise Ratio How many colors we possibly represent in a digital image? The number of bits used to represent each sample The sample size or bit depth The different values correspond to color levels or sound amplitudes Stereo CD-quality digital audio uses 16 bits per sample in each of the two stereo channels, for a total of 32 bits per sample 22

12 Quantization, Quantization Error, and Signal-to-Noise Ratio (Cont d) Sample size affects how precisely the value of a sample can be represented In digital sound how much you have to round off the amplitude of the wave when it is sampled at various points in time In digital image how close the digital image s colors are to the original colors that it is supposed to represent 23 24

13 Quantization, Quantization Error, and Signal-to-Noise Ratio (Cont d) The amount of error implicit in a chosen bit depth can be measured in terms of the signal-to-noise ratio (SNR) SNR is defined as the ratio of the meaningful content of a signal versus the associated noise Noise is the part of the message that is not meaningful; it gets in the way of the message intended in the communication 25 Quantization, Quantization Error, and Signal-to-Noise Ratio (Cont d) SNR In analog data communication SNR is defined as the ratio of the average power in the signal versus the power in the noise level For a digitized image or sound SNR is defined as the ratio of the maximum sample value versus the maximum quantization error also be called signalto-quantization-noise ratio (SQNR) 26

14 Quantization, Quantization Error, and Signal-to-Noise Ratio (Cont d) SQNR Be measured in terms of decibels abbreviated db I E 1 db = 10 log10 = 20 log I0 E0 where I and I 0 are the intensities or powers across a surface area, E and E 0 are amplitude, potential, or pressure, measured in volts 27 Quantization, Quantization Error, and Signal-to-Noise Ratio (Cont d) SQNR SQNR ( quantization value) ( quantization error) max = 20 log10 = 20 log10 2 max n ( ) Dynamic Range The fewer bits you have to represent a range of colors or a range of sound samples, and the wider the range of colors or sound you want to represent, the less ability you have to represent subtle differences between values. 28

15 29 Data Storage 30

16 31 Data Communication Data communication Straddle the disciplines of computer science and electrical l engineering i Analog compared with Digital Data Communication The transmission medium does not determine whether the data is communicated in analog or digital form Across copper wire or coaxial cable, data can be transmitted by changing voltages Through h optical fiber, data can be communicated by a fluctuating beam of light 32

17 Data Communication (Cont d) Analog compared with Digital Data Communication (Cont d) Analog Sound is captured electronically, the changes in air pressure are translated to changes in voltage. The voltage changes are continuous in the same way that the waveform is continuous 33 Data Communication (Cont d) Analog compared with Digital Data Communication (Cont d) Digital Sound is sampled and quantized such that the data are transformed into a sequence of 0s and 1s. Over copper wire, these 0s and 1s are sent by means of an electrical current and con be represented as two discrete voltage levels Varying the voltage in two levels is called baseband transmission,, and the line of communication between sender and receiver is called a baseband channel 34

18 Data Communication (Cont d) Analog compared with Digital Data Communication (Cont d) Baseband transmission Work well only across relatively short distances Noise and attenuation cause the signal to degrade as it travels over the communication channel Attenuation is the weakening of a signal over time and/or space Modulated data transmission (or bandpass transmission) ) signal degrades more slowly and thus is better for long distance communication 35 Data Communication (Cont d) Analog compared with Digital Data Communication (Cont d) Modulated data transmission make use of a carrier signal on which data are written Data are written on the carrier signal by means of modulation techniques Amplitude modulation (AM) Frequency modulation (FM) Phase modulation (PM) 36

19 Data Communication (Cont d) Analog compared with Digital Data Communication (Cont d) When a digital 1 is communicated AM the amplitude of the carrier signal is increased FM the frequency is changed PM the phase is shifted 37 38

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21 Data Communication (Cont d) Bandwidth Bandwidth as Maximum Rate of Change in Digital Data Communication Transmission of discrete 0s and 1s Can be done by discrete pulses Discrete change of voltages in baseband data transmission Discrete changes in frequency, amplitude, or phase of a carrier signal How fast can the signal be changed? and the receiver must be able to understand the changing signal 41 Data Communication (Cont d) Bandwidth How fast can the signal be changed? The physical properties of the transmission medium The engineering of the sending and receiving devices The system bandwidth a maximum rate of change for that communication system 42

22 Data Communication (Cont d) Bandwidth Measured in cycles per second or Hz It can change its signal from one voltage level to a different one and then back again The data rate d in bits/s is d = 2b assume that a signal is sent with two possible signal levels and a bandwidth of b Hz. 43 Data Communication (Cont d) Bandwidth Measured in cycles per second or Hz (Cont d) Multilevel coding allows more than multiple signal levels such that more than one bit can be communicated at a time The data rate d in bits/s is ( ) assume that a d signal = 2b is log sent 2 with k k possible signal levels and a bandwidth of b Hz. 44

23 45 Data Communication (Cont d) Bandwidth Bandwidth of a Signal in Terms of Frequency A signal is sent in the form of a wave Any complex periodic waveform can be decomposed both mathematically and physically into frequency components that are simple sine waves The bandwidth of a signal is the difference between the maximum and minimum frequency components. The width of a signal w is w = f f max min 46

24 Data Communication (Cont d) Bandwidth Bandwidth of a Communication Channel in Term of Frequency Data are sent along some particular channel, which is a band of frequencies. The range of frequencies allocated to a band constitutes the bandwidth of a channel (or width of a channel) 47 48

25 Data Communication (Cont d) Data Rate Bit Rate Bandwidth is measured in cycles per second Hz; data rate is measured in bits per second; e.g., kb/s, kb/s, Mb/s, MB/s, Shannon s theorem quantifies the achievable data rate for a transmission system that introduce noise s c= blog 2 1+ p s is a measure of the signal power; p is a measure of the noise power 49 50

26 Data Communication (Cont d) Data Rate Baud Rate The number of changes in the signal per second, as a property of sending and receiving devices A property measured in cycles per second, Hertz (Hz) 51 Compression Methods Types of Compression For good fidelity to the original source source must be digitized at a fine degree of resolution and with quantization levels covering a wide dynamic range They need to be made smaller 52

27 Compression Methods Types of Compression Lossless compression no information is lost between the compression and decompression steps Lossy compression sacrifice some information. However, the information lost is not important to human perception E.g., dictionary-based, entropy, arithmetic, adaptive, perceptual, p differential compression method, etc 53 Compression Methods Types of Compression Compression rate the ratio of the original file size a to the size of the compressed file b, expressed as a:b or the ratio b to a as a percentage 54

28 Compression Methods Run-Length Encoding (RLE) Lossless compression E.g.,.BMP file Assume a grayscale file One byte per pixel, 0 to 255 grayscale values Dimensions Row-major order the values from a whole row are stored from left to right, then the next row from left to right, and so forth 55 Compression Methods Run-Length Encoding (RLE) Instead of storing each pixel as an individual value, it store number pairs (c, n) the grayscale value and indicate how many consecutive pixels have that value E.g., The RLE of this sequence would be (255, 6), (242, 4), (238, 6), (255, 4) 56

29 Compression Methods Run-Length Encoding (RLE) Without RLE, 20 pixels require 20 bytes Consider (c, n) pair c can be stored in one byte n can be 10,000 pixels which need 14 bits then round up to two bytes The compressed data is 12 bytes rather than 20 bytes 57 Compression Methods Run-Length Encoding (RLE) Practically, setting the bit depth of n in advance If more than n consecutive pixels of the same color are encountered, the runs are divided into blocks. E.g., if one byte is used to represent each n, if 1000 consecutive whites exist in the file, they would be represented as (255,255), (255,255), (255,255), (255, 235) 58

30 Compression Methods Run-Length Encoding (RLE) E.g., disregarding heading information, Require 1084 bytes The compression ratio is about 9:1 59 Compression Methods Entropy Encoding Claude Elwood Shannon ( ) Boolean Algebra Information Theory Work by mean of variable-length codes Using fewer bits to encode symbols that occur more frequently Using more bits for symbols that occur infrequently 60

31 Compression Methods Entropy Encoding Let S be a string of symbols and p i be the frequency of the i th symbol in the string. 1 = = i p i ( ) η pi log2 H S p i can equivalently be defined as the probability that the i th symbol will appear at any given position in the string. 61 Compression Methods Entropy Encoding E.g., g, an image file has 256 pixels, each pixel of a different color. Then, the frequency of each color is 1/256 H S = log2 ( log2 256 ) 8 1 = = ( ) ( ) The average number of bits needed to encode each color is eight. 62

32 Compression Methods Entropy Encoding Color Frequency Optimum Relative Product of Number of Bits to Encode This Color Frequency of the Color in the File Column 3 and 4 Black White Yellow Orange Red Purple Blue Green Compression Methods Entropy Encoding Then, Shannon s equation becomes = log2 + log2 + log2 + log ( ) log2 log2 log2 log2 H S This would be an optimum encoding Overall, the minimum value for the average number of bits required to represent each symbol-instance in this file is

33 Compression Methods Entropy Encoding Not necessary to use the same number of bits to represent each symbol. A better compression ratio is achieved if we use fewer bits to represent symbols that appear more frequently in the file 65 Compression Methods Entropy Encoding (Shannon-Fano Algorithm) Color Frequency Code Black White Yellow Orange Red Purple Blue green

34 Compression Methods Entropy Encoding (Shannon-Fano Algorithm) How close is the number of bits per symbol to the minimum of 2.006? The colors require 584 bits to encode For 256 symbols in the file, this is an average of 584/256 = 2.28 bits per symbol-instance Assume that before compression, eight bits were used for each symbol, then the compression rate is 8/2.28, which is about :1 67 Compression Methods Arithmetic Encoding Based on a statistical analysis of the frequency of symbols in a file like the Shannon-Fano algorithm Unlike the Shannon-Fano algorithm, encoding an entire file (or string of symbols) as one entity rather than creating a code symbol by symbol The idea is that a string of symbols will be encoded in a single floating point number! 68

35 Compression Methods Arithmetic Encoding Frequencies of Colors Relative to Number of Pixels in File Color Frequency Out of Total Number of Probability Interval Assigned to Pixels in File Symbol Black (K) 40/100 = White (W) 25/100 = Yellow (Y) 15/100 = Red (R) 10/100 = Blue (B) 10/100 = Compression Methods Arithmetic Encoding Range Values for encoding the example problem (W, K, K, Y, R, B) Low Value for Probability Interval High Value for Probability Interval Symbol 1 0 = *0.4 = *0.65 = 0.65 White = *0 = *0.4 = 0.5 Black = *0 = *04 = 0.44 Black = *0.65 = *0.8 = Yellow = * = * = Red = *0.9 = *1 = Blue The low value of the range can be taken as the encoding of the entire string 70

36 Compression Methods Arithmetic Encoding 71 Compression Methods Arithmetic Encoding Values for decoding in the example problem (W, K, K, Y, R, B) Floating Point Number f, Representing Code Symbol Whose Probability Interval Surrounds f Low Value for Symbol s Probability Interval High Value for Symbol s Probability Interval Size of Symbol s Probability Interval W ( )/( ) = K ( )/(0.4 0) = K ( )/(0.4 0) = Y ( )/( ) = R ( )/( ) = 0.95 B

37 Compression Methods Transform Encoding Lossless methods do not always give enough compression, esp. for large audio and video files. Lossy method are needed; they are designed so that the information lost is relatively unimportant to the perceived quality of the sound or images. Lossy methods are often based upon transform encoding The idea is that changing the representation of data can sometimes make it possible to extract sounds or visual details that won t be missed because they are beyond the acuity of human perception p 73 Compression Methods Transform Encoding This type of compression begins with a transform of the data from one way of representing it to another. Two most commonly used transforms in digital media are Discrete Cosine Transform (DCT) Discrete Fourier Transform (DFT) It is not the transform that is lossy. When a transform is used as one step in a compression algorithm, it becomes possible to discard d redundant d or irrelevant information in later steps, thus reducing the digital file size. This is the lossy part of the process. 74

38 Standards and Standardization Organizations for Digital Media Standards can be divided into three main types Proprietary Set and patented by commercial companies; e.g., LZW De facto A method or format that has becomes the accepted way of doing things in the industry without any official endorsement; e.g., TIFF Official Developed by large industry consortia and/or government agencies; e.g., ITU, ISO, IEC 75