The Science of Digital Media Course Book Details • • Title: The Science of Digital Media Author: Jennifer Burg Publisher: Pearson International Edition Publication Year: 2009 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 1
The Science of Digital Media General Course Contents • Part-I: Digital Data Representation and Communication • Part-II: Digital Image Representation • Part-III: Digital Image Processing • Part-IV: Digital Video Processing 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 2
The Science of Digital Media General Course Contents • Part-I: Digital Data Representation and Communication Analog to Digital Conversion Data Storage Data Communication Compression Methods Standards and Standardization Organization for Digital Media – Mathematical Modelling Tools for the Study of Digital Media – – – 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 3
The Science of Digital Media Analog to Digital Conversion • Analog versus Discrete Phenomena • Image and Sound Data represented as Functions and Waveforms • Sampling and Aliasing • Quantization, Quantization Error, and Signal -to-Noise Ratio 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 4
The Science of Digital Media Analog to Digital Conversion • Analog versus Discrete Phenomena – Analog Phenomena • are continuous, eg. , stead stream of water, a line on the graph or a continuous rotating dial on a radio – no clear separation between one point and the next – no separation between any two points, there is an infinite number of other points exist – Discrete phenomena • are clearly separated – there is a point (in space or time) – there are neighbouring point – there is nothing between two points 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 5
The Science of Digital Media Analog to Digital Conversion • Analog versus Discrete Phenomena – Analog-to-Digital conversion • Converting the continuous phenomena of images, sound and motion into a discrete representation that can be handled by computer – Advantages of Digital media over Analog • possibility to increase digital media resolution (due to increase media storage and data rate in communication channels) • image and sound are communicated 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 6
The Science of Digital Media Analog to Digital Conversion • Analog versus Discrete Phenomena – Advantages of Digital media over Analog • analog data communication is vulnerable to noise than digital, so it looses some of its quality in transmission • digital data is communicated entirely with 0 s and 1 s, error-correcting strategies is possible to ensure data is received and interpreted correctly • digital data can be communicated more compactly than analog (excellent compression algorithms) • provides varying bandwidth among various broadcasts to consumers 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 7
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – primary media in digital media are Images and Sound i. e. , IMAGE + SOUND = VIDEO – both images and sound can be represented as functions visualized by their corresponding graphs – Sound is a one-dimensional function i. e. , a function with one variable as input 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 8
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Taking sound as a continuous phenomenon, then it corresponds to continuous function: where is time and is the air pressure amplitude – The essential form of function representing sound is sinusoidal i. e. , has a shape of sine wave. Consider a triangle in a Unit Cycle 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 9
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Sines and Cosines are called Sinusoidal functions sin( ) = c/h cos( ) = a/h h A -axis c -axis C 10 February 2010 a B Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 10
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – According to Pythagorean theorem the equation for Unit Cycle is – As you move “Q” around the Unit cycle counterclockwise, angle goes from 0 to (in radians) – For multiple times of rounds where “k” is number of times (“k” is +ve in counterclockwise and –ve otherwise) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 11
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Generalized definitions of sine and cosine are: – If and “k” is an integer then 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 12
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Sine and cosine functions are periodic (their values cycle in regular pattern as indicated in the table below – Angle conversion formula from Radians to Degree and vice versa: where: r = angle in radian and d = angle in degrees Angle in Radians 0 Angles in Degrees 0 30 45 60 90 120 135 150 180 210 225 240 270 300 315 330 360 Sine of Angle 0 0 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 13
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Sine and Cosine angles visualization is as indicated in the figure below: • x-axis represents the size of the angle while y-axis represents the sine of the angle 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 14
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – How Sinusoidal function relates to wave and thus to sound and images? – Sound is a Mechanical Wave • i. e. , it results from the motion of particles through a transmission medium eg. , the motion of molecules in air • sound cannot be transmitted through vacuum • movement associated with sound wave is initiated by a vibration, consider a vibrating string, its wave swings left to right and vice versa 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 15
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Sound is a Mechanical Wave • when wave is moving from left to right, air molecules are pushed next to each other, hence pressure rises, when string moves right to left, air molecules spread out, hence pressure is reduced 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 16
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – The periodic changing of air pressure – high to low, etc. , forms a mechanical wave – Below is a diagram of single-frequency (440 Hz) tone with no overtones, represented as a waveform 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 17
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – The motion of the air molecules is back and forth from left to right -> to the direction in which the wave is radiating out from string – Longitudinal wave • A wave in which the motion of individual particles is parallel to the direction in which energy is being transported The wave is periodic if it repeats a pattern over time The pattern that is repeated constitutes one cycle of the wave Wavelength is the length (in distance) of one complete cycle The frequency of a wave is the number of times a cycle repeats per unit time (in the case of sound, is the rate at which air molecules that are vibrating). • Frequency is measure in cycles per second or Hertz • • 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 18
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Abbreviations for Frequency or Sampling rate Hertz kilohertz megahertz second millisecond Hz k. Hz MHz s microsecond ms nanosecond ns 1 Hz = 1 cycle/s 1 KHz = 1000 Hz 1 MHz = 1, 000 Hz – Period of a wave is the amount of time it takes for one cycle to complete. Period and frequency are reciprocals of each other where T = period and f = frequency 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 19
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Amplitude is the height of the wave – In order to create a sine function representing a sound wave of frequency f Hz, you must convert to angular frequency first. Where is the angular frequency in Radians/s and is frequency of a sine wave measured in Hz. – The amplitude of the wave corresponds to sound loudness The larger to amplitude the louder the sound – Frequency of the wave corresponds to the pitch of the sound The higher the frequency the higher-pitched the sound 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 20
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Single-frequency tone waves can be added to form more complex waveform – A complex waveform can be reversed by breaking it down mathematically into frequency components by a method called Fourier transform – The simple sinusoidal waves are called the frequency components of the more complex wave 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 21
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Fourier transform • makes it possible to store a complex sound wave in digital form • determine the wave’s frequency components • filters out components that are not wanted (improves quality or compresses digital audio file) – Sinusoidal waveforms are used to represent changing color amplitudes in digital images too 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 22
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Regardless of the medium, analog-to-digital requires the same two steps Sampling and Quantization – Sampling • Chooses discrete points at which to measure a continuous phenomenon (called signal) – For images the sample points are evenly separated in space – For sound the sample points are evenly separated in time • Sampling rate (or the resolution) is the number of samples taken per unit time or unit space 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 23
The Science of Digital Media Analog to Digital Conversion • Image and Sound Data Represented as Functions and Waveforms – Quantization • Requires that each sample be represented in a fixed number of bits, called the sample size or equivalently the bit depth • Bit depth is for limiting precision with which each sample can be represented 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 24
The Science of Digital Media Analog to Digital Conversion • Sampling and Aliasing – Sampling • a process of converting a signal (e. g. , a function of continuous time or space) into a numeric sequence (a function of discrete time or space) • Undersampling means the sampling rate did not keep up with the rate of change of pattern in the image or sound – Aliasing • In digital image arises from undersampling and results in an image that does not match the original source, it may be blurred or have a false pattern similarly for audio wave 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 25
The Science of Digital Media Analog to Digital Conversion • Sampling and Aliasing – Nyquist Theorem • It specifies the sampling rate needed for a given spatial or temporal frequency • It states that to guarantee that no aliasing will occur, you must use a sampling rate that is greater that twice the frequency of the signal being sampled 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 26
The Science of Digital Media Analog to Digital Conversion • Sampling and Aliasing – Nyquist Theorem • The Nyquist theorem applied to a single-frequency, one dimensional wave is summarized in the following equation: where = minimum sampling rate and = frequency of sine wave is called the Nyquist frequency • Nyquist theorem applies equally to digital images 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 27
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Quantization • Quantization is the second step in analog-to-digital conversion • For digital images, each sample represents a color at a discrete point in a two dimensional image • Number of colors possible is determined by the sample size or bit depth (color depth for images) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 28
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Quantization • One bit of color per sample == two colors because a bit has two values 0 or 1. Eight bits, then 28 = 256 colors possible, etc • In general, if n is the number of bits used to quantize a digital sample, then the maximum number of different values that can be represented, m, is m = 2 n • The large the bit depth, the more subtle the color changes can be in a digitized image, the bigger the file size • For digital audio, the common sample sizes are 8 and 16 bits 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 29
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Quantization Error • is the difference between the actual analog value and quantized digital value • the error is due either to rounding or truncation. It is sometimes considered as an additional random signal called quantization noise 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 30
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Quantization Error 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 31
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Signal-to-Noise Ratio (SNR) • Is the ratio of the meaningful content of a signal versus the associated noise – For analog is the ratio of the average power in the signal versus the power in the noise level. Think of a signal send over a network compared to the extend in which the signal is corrupted – For digitized image or sound, is the ratio of maximum sample value versus the maximum quantization error. The ratio depends on the bit depth. It is also called signal-to-quantization-noise ratio (SQNR) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 32
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Signal-to-quantization-noise ratio (SQNR) • Is measured in terms of decibels (d. B). A d. B is a dimensionless unit, they cancels in division • A d. B is used to describe the relative power or intensity of two phenomena. Where I and I 0 are the intensities (power across a surface area) of two signals of sound, data signal on a communication network or output of lasers etc, measured in watts 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 33
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Signal-to-quantization-noise ratio (SQNR) • Another definition for decibels is: Where E and E 0 are amplitude, potential or pressure in volts • The two definitions are equivalent, take the relationship between power I, potential E and resistance R 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 34
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Signal-to-quantization-noise ratio (SQNR) • Assuming that R is constant for the two signals, then 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 35
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Signal-to-quantization-noise ratio (SQNR) • Using the second definition of decibels, SQNR applies to linearly quantized samples • The sample values range from with ‘n’ bits for quantization • Audio signal in sine wave goes from +ve to –ve values • Maximum quantization error is half a quantization level 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 36
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Signal-to-quantization-noise ratio (SQNR) • Therefore, In short, let ‘n’ be the bit depth of digitized media file (e. g. , digital audio) then SQNR is: 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 37
The Science of Digital Media Analog to Digital Conversion • Quantization, Quantization Error and Signal-to. Noise Ratio – Signal-to-quantization-noise ratio (SQNR) • SQNR is directly related to Dynamic range • Dynamic range is the ratio of the largest-amplitude sound(or color, for digital images) and the smallest that can be represented with a given bit depth. 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 38
The Science of Digital Media Data Storage • Digital media requires the handling of large amount of data – See example of File sizes for Uncompressed Digital Image, Audio and Video in the table below Image File Audio File Video File Resolution: 1024 pixels x 768 pixels Total Number of Pixels: 786, 432 Color mode: RGB Bits per pixel: 24 (i. e. , 3 bytes) Total number of bits: 18, 874, 368 (=2, 359, 296 bytes) File size: 2. 25 MB Sampling rate: 44. 1 k. Hz (44, 100 samples per second) Bit depth: 32 bits per sample (16 for each of two stereo channels) (i. e. , 4 bytes) Number of minutes: one Total number of bits: 84, 672, 000(=10, 584, 000 bytes) File size: 10. 09 MB Data rate of the file: 1. 35 Mb/s Frame size: 720 pixels x 480 pixels Bits per pixel: 24 Frame rate: ~30 frames/s Number of minutes: One Total image requirement: 14, 929, 920, 000 bits Audio requirement: 84, 672, 000 (see column 2) Total number of bits: 15, 014, 592, 000(=1, 876, 824, 000 bytes) File size: >1. 7 GB Data rate of the file: 238. 65 Mb/s 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 39
The Science of Digital Media Data Storage • Digital media requires the handling of large amount of data – Table below shows common abbreviations for data sizes kilobyte megabyte gigabyte kilobit megabit gigabit terabyte k. B MB GB kb Mb Gb Tb TB For memory and file sizes assume the following 1 byte = 8 bits 1 k. B = 210 bytes 1 MB = 220 bytes 1 GB = 230 bytes 1 TB = 240 bytes = 1024 bytes = 1, 048, 576 bytes = 1, 073, 741, 824 bytes = 1, 099, 511, 627, 776 bytes kb, Mb, Gb and Tb are defined analogously 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 40
The Science of Digital Media Data Storage • Digital media requires the handling of large amount of data – Confusion between the prefixes kilo-, mega-, and gigaeg. , for the case of Hertz: kilo- means 103 = 1000 mega- means 106 = 1, 000 giga- means 109 = 1, 000, 000 – In the case of data storage kilo- means 103 or 210 mega- means 106 or 220 giga- means 109 or 230 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 41
The Science of Digital Media Data Storage • Digital media requires the handling of large amount of data – Manufacturers wants to make their storage media look larger, so they generally use the power of 10 – While many computers will give file sizes defined in powers of 2 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 42
The Science of Digital Media Data Communication • The importance of Data Communication in the study of Digital Media – Digital files are typically very large, can be stored in CDs and DVDs, send them in email, and post them on web pages -> consideration to transmission media – Sound and video are time-based media, they require large amount of data. • Capturing and transmitting in real-time require data transmission rate is the same as that of which data is played • Consideration is taken to bandwidth and data rate – Digital communication media at home and offices • Cellular phones, digital cable, digital television, HDTV and more 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 43
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – Whether data is in analog or digital, they both need a communication channel from sender to receiver e. g. , • Land-based or cellular telephone • Shortwave or regular radios – Cable – terrestrial or satellite television – wired or wireless computer networks 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 44
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – How do you know which communication are being send digitally? • Transmission medium does not determine the form of data, digital or analog – Copper wire – can transmit both analog and digital data (eg. , telephone or computer networks) – Coaxial cable (e. g. , television) – Optical fiber (e. g. , high-speed computer networks) – Free space (e. g. , radio or television) • Copper wire, coaxial cable and optical fiber all require a physical line between the sender and receiver 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 45
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – How do you know which communication are being send digitally? • Across copper wire or coaxial cable, data can be transmitted by changing voltages • Through Optical fiber, data can be communicated by a fluctuating beam of light • Free space, data can be communicated through electromagnetic waves sent by satellite or radio transmission – It is the representation of data, not the transmission medium that determine if communication is analog or digital 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 46
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • Take analog telephone transmissions through wire to start with • First sound is captured electronically, changes in air pressure are tranlated to changes in voltage • For the spoken word “boo, ” the voltages rise and fall as indicated in the figure below 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 47
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • If the word “boo, ” is digitized, it is sampled and quantized such that data are transformed into sequence of 0 s and 1 s as in figure below • +V (voltage level) may represent 1 bit and –V(voltage level) may represent 0 bit • Communication begins with some initial sychronization between sender and receiver 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 48
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • A sending device maintains a steady voltage for a fixed amount of time to send each bit • The receiving device samples the transmission at evenly-spaced points in time to interpret whether 0 or 1 has been sent • Varying the voltage levels in the manner just described is called Baseband transmission • The line of communication between sender and receiver is called a Baseband channel • Baseband transmission is used across wire and coaxial cable, across relatively short distances (due to noise and attenuation) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 49
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • Attenuation is the weakening of a signal over time and/or space • Modulated data transmission (or bandpass transmission) – Is based on the observation that a continuously oscillating signal degrades more slowly and thus is better for long distance communication • Modulated data transmission makes use of a carrier signal on which data are “written” • Data are written on the carrier signal by means of modulation techniques 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 50
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • Three basic methods for modulating a carrier wave are : – Amplitude modulation, Frequency modulation and Phase modulation • Amplitude Modulation – The amplitude of the carrier signal is increased by a fixed amount each time a digital 1 is communicated • Frequency Modulation – The frequency is changed • Phase Modulation – The phase is shifted 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 51
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • Figure below shows the modulation methods where digital signal 101 is being send Amplitude Modulation 10 February 2010 Frequency Modulation Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer Phase Modulation 52
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • Modulated signals are not necessary digital • Bandpass tramission -> the carrier signal lies in the center of a frequency band called a channel that is allocated for communication • The sender and receiver both know the channel assigned to them • The sender uses only those frequencies that lie within its channel, and the receiver listens only within that channel 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 53
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • The spectrum of visible light – Different colors of light have different frequencies – Color of light are divided into bands or channels when communicated along optical fiber, see figure below – The figure shows colors by their wavelength Where is Wavelength, is frequency and is the speed of light in vacuum 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 54
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • Electromagnetic Waves – can be divided into frequency bands also – both analog and digital messages can be encoded using carrier signals in the form of light or other electromagnetic waves – a continuously oscillating electrical voltage can also be used as a carrier signal (analog telephone to handle digital data by means of modem case) – Modem stands for Modulator and Demodulator – Modem takes data given to it by a computer and writes the 0 s and 1 s onto continuously oscillating voltage using one of the three modulation methods – At the other end of the call another modem demodulates the signal for delivery to another computer 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 55
The Science of Digital Media Data Communication • Analog compared with Digital Data Communication – What is the difference between ways analog and digital data are transmitted across a network? • Figure showing the Electromagnetic Spectrum 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 56
The Science of Digital Media Bandwidth • Bandwidth as Maximum Rate of Change in Digital Data Communication – In digital media, bandwidth refers to transmission of discrete 0 s and 1 s – Transmission can be done by discrete pulses, i. e. , discrete changes of voltages in baseband data transmission – In case of modulated communication, • Data can be communicated by discrete changes in frequency, amplitude or phase of a carrier signal 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 57
The Science of Digital Media Bandwidth • Bandwidth as Maximum Rate of Change in Digital Data Communication – How fast can the signal be changed from voltage +V to –V and back again? – How fast can the sender change the amplitude of a carrier signal (or the frequency or the phase)? Keeping in mind that the receiver will understand the changing signal as well! – Example 1: • How fast can you talk and still speak clearly? (that your friend can understand!) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 58
The Science of Digital Media Bandwidth • Bandwidth as Maximum Rate of Change in Digital Data Communication – The maximum rate at which you can talk and your friend can understand is the bandwidth of communication. • Note: this has nothing to do with the speed of sound! – Example 2: • What if you had to send Jarkko code by means of a blinking flashlight? How fast could you send the code? – The speed is limited by how fast the hardware (your flashlight) can be operated and how fast your hand can click • Note: this has nothing to do with the speed of light 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 59
The Science of Digital Media Bandwidth • Bandwidth as Maximum Rate of Change in Digital Data Communication – Bandwidth is measured in cycles per second or Hz – A baseband transmission system with a bandwidth of 5000 Hz means it can cycle through its signal (from one voltage level to another and back again at the rate of 5000 times per second – In general if a signal is send with two possible signal levels then: d is the data rate, in bits/s b is bandwidth in Hz 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 60
The Science of Digital Media Bandwidth • Bandwidth as Maximum Rate of Change in Digital Data Communication – Bandwidth is defined by how fast the signal can change – What if more than one signal level is permitted? (Instead of having one voltage level represent 0 and the other 1, you have 00, 01, 10 and 11 voltage levels) – Therefore, each change of voltage would transmit two bits instead of one – Multilevel Coding -> Means allowing more than two signal levels such that more than one bit can be communicated 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 61
The Science of Digital Media Bandwidth • Bandwidth as Maximum Rate of Change in Digital Data Communication – Assuming that a signal is sent with “k” possible signal levels and a bandwidth of “b” Hz. Then the data rate, “d”, in bits/s is: 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 62
The Science of Digital Media Bandwidth • Bandwidth as Maximum Rate of Change in Digital Data Communication – Figure showing an example of data rate as determined by number of signal levels With k signal level, log 2 k bits are transmitted with each signal. 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 63
The Science of Digital Media Bandwidth • Bandwidth of signal in terms of Frequency – Bandwidth of a signal (Width of a signal) • Is the difference between the maximum and minimum frequency components of a periodic wave form • Generally: Where is the Width of a signal is the frequency of the highest-frequency component is frequency of the lowest-frequency component • The width of a signal must fit within the width of the channel on which it is transmitted otherwise some information will be lost 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 64
The Science of Digital Media Bandwidth • Bandwidth of a Communication Channel in Terms of Frequency – Data is communicated via airwaves through particular channel, i. e. , band of frequencies – The range of frequencies allocated to a band constitutes the bandwidth of a channel( or width of a channel because it correlates with width of signal) – Bandwidth in this case refers to data that are transmitted by means of a carrier signal of a given frequency that lies at the center of channel 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 65
The Science of Digital Media Bandwidth • Bandwidth of a Communication Channel in Terms of Frequency – The Federal Communication Commission allocates channels of an appropriate bandwidth, enough to accommodate the type of communication, see next table Frequency Bands for Radio and Television Radio Television AM, 535 k. Hz to 1. 7 MHz shortwave radio, 5. 9 MHz to 26. 1 MHz CB radio, 26. 96 MHz to 27. 41 MHz FM radio, 88 MHz to 108 MHz, allocated in 200 k. Hz channels 54 to 88 MHz for channels 2 to 6 174 to 216 MHz for channels 7 to 13 470 to 890 MHz for UHF channels 14 to 83 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 66
The Science of Digital Media Bandwidth • Bandwidth of a Communication Channel in Terms of Frequency – Also carrier signal that lies at the center of channel caries data (analog or digital data) – Modulation is applied to carrier signal so that it contains data, regardless of whether it is analog or digital – Modulation adds frequency component called sidebands to the original carrier signal – Sidebands must lie within the designated channel – The bandwidth of a channel affects the amount of information that can be communicated 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 67
The Science of Digital Media Bandwidth • Bandwidth of a Communication Channel in Terms of Frequency – How is an appropriate bandwidth determined for AM radio, FM radio, Television and digital HDTV? – What makes 10 k. Hz (for AM radio), 200 k. Hz (for FM radio), 6 MHz(for television) and 20 MHz (for digital HDTV), the right size? – How does modulation of a carrier signal give rise to sidebands? – What are the frequencies of these sidebands and their effect to bandwidth requirements for channels? – These questions will be examined in more details in Chapter 6 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 68
The Science of Digital Media Data Rate • Bit Rate – Taking the first Bandwidth definition • The maximum rate of change of a signal, as a property of the communication system on which the signal is being sent – The definition is closely related to data rate or bit rate – Bandwidth is often loosely used as a synonym for data rate or bit rate – But we are going to distinguish between the terms 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 69
The Science of Digital Media Data Rate • Bit Rate – Bandwidth is measured in cycles per second – Hertz – Data rate is measured in bits per second – more precisely, • • • in kilobits per second (kb/s) in kilobytes per second (k. B/s) megabits per second (Mb/s) megabytes per second (MB/s) gigabits per second (Gb/s) gigabytes per second (GB/s) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 70
The Science of Digital Media Data Rate • Bit Rate – Bandwidth and data rate are related by the equation – d is a theoretical data rate – a maximum that is not achievable in reality – The actual amount of data that can be sent per unit time is limited by the noise that is present in any communication system – No signal can be send with a perfect clarity over an indefinite span of space and time – Some amount of noise is introduced by electromagnetic interference 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 71
The Science of Digital Media Data Rate • Bit Rate – If too much noise is introduced, the receiver cannot always interpret the signal correctly – A refinement of the relationship between data rate and bandwidth is given by Shannon’s Theorem, quantifies the achievable data rate for a transmission system that introduces noise: Where is a measure of the signal power is a measure of the noise power – Note that is another application of signal-to-noise ration discussed earlier 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 72
The Science of Digital Media Data Rate • Bit Rate – Data rate is important in three aspects of digital media • Communicating the data • Capturing the data • In case of audio and video playing it – No one wants to wait an unreasonable length of time to transfer pictures, sound and video from one place to another – Because digital data are large, compression becomes important aspect to achieve three important aspects of data rate above 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 73
The Science of Digital Media Data Rate • Baud Rate – Baud rate has a close meaning to bandwidth and bit rate – Is the number of changes in the signal per second, as a property of sending and receiving devices, measured in cycles per second, Hertz – Under this definition baud rate is synonymous with bandwidth, not bit rate 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 74
The Science of Digital Media Data Rate • Baud Rate – The main difference to bandwidth is that baud rate is usually used to refer to sending and receiving devices, whereas bandwidth has other meanings related to frequencies over the airwaves – A device like a modem can have a maximum baud rate as well as an actual baud rate. The actual baud rate is the rate agreed upon between sender and receiver for a particular communication – What is often reported as a baud rate is really a bit rate. (But bit rate is generally what you want to know anyway, so no harm done). To be precise, baud rate and bit rate are related by the equation: 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 75
The Science of Digital Media Compression Methods • Types of Compression – Digital media files are usually very large, they need to be made smaller – compressed – Without compression -> storage capacity will not be enough and communicating them across network will be difficult – On the other hand, you do not want to sacrifice the quality of your digital images, audio and video in the compression – Fortunately, the size of the digital files can be reduced significantly with little or no perceivable loss of quality 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 76
The Science of Digital Media Compression Methods • Types of Compression – Compression algorithms can be divided into two basic types: • Lossless Compression – No information is lost between the compression and decompression steps – Compression reduces the file size to fewer bits. – Decompression restores the data values to exactly what they were before the compression 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 77
The Science of Digital Media Compression Methods • Types of Compression – Compression algorithms can be divided into two basic types: • Lossy Compression – Sacrifices some information – The algorithm is designed so that the information lost is not generally important to human perception » In image files, it could be subtle changes in color that the eyes cannot detect » In sound files, it could be the frequencies that are imperceptible to the human ear 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 78
The Science of Digital Media Compression Methods • Types of Compression – Other labels given to types of compression algorithms are: • • • Dictionary-based compression Entropy compression Arithmetic compression Adaptive compression Differential compression methods 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 79
The Science of Digital Media Compression Methods • Types of Compression – Other labels given to types of compression algorithms are: • Dictionary-based compression method (e. g. , LZW compression) – Uses a look-up table of fixed-length codes – One code word may correspond to a string of symbols rather than to a single symbol in the file being compressed • Entropy compression – Uses a statistical analysis of the frequency of symbols and achieves compression by encoding more frequently-occuring symbols with shorter code words, with one code word assigned to each symbol – Shannon-fano and Huffman encoding are examples of Entropy compression 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 80
The Science of Digital Media Compression Methods • Types of Compression – Other labels given to types of compression algorithms are: • Arithmetic Encoding – Benefits from similar statistical analysis, but encodes an entire file in a single code word rather creating a separate code for each symbol • Adaptive Method – Gains information about the nature of the file in the process of compressing it, and adapt the encoding to reflect what has been learned at each step – LZW compression is by nature adaptive because the code table is created “on the fly” during compression and decompression – Huffman encoding can be made adaptive if frequency counts are updated as compression proceeds rather than being collected beforehand, it can adapt the nature of data as it reads 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 81
The Science of Digital Media Compression Methods • Types of Compression – Other labels given to types of compression algorithms are: • Differential Encoding – Is a form of lossless compression that reduces file size by recording the difference between neighbouring values rather than recording the values themselves – Differential encoding can be applied to digital images, audio or video – The Compression rate of the compression algorithm • Is the ratio of the original file size “a” to the size of compressed file “b” expressed as a: b. • Alternatively you can speak of the ratio of b to a as a percentage 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 82
The Science of Digital Media Compression Methods • Run-Length Encoding (RLE) – Is a simple example of lossless compression – Is used in image compression e. g. , . bmp suffix ( a Microsoft version of bitmap image files uses RLE) – How RLE Works? • An image file is stored as a sequence of color values for consecutive pixel locations across rows and down columns • If the file is in RGB color mode –> three bytes per pixel, one for each of the Red, Green and Blue color channels • If the file is grayscale -> one byte per pixel • For simplicity a grayscale file is used for this demonstration of RLE 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 83
The Science of Digital Media Compression Methods • Run-Length Encoding (RLE) – How RLE Works? • Each pixel position is encoded in one byte, it represents one of the 256 grayscale values (28=256 different things) • Grayscale image file consists of a string of numbers each of them between 0 and 255 • Assume that image has a dimension of 100 x 100 for a total of 10, 000 pixels • Assume also that the pixels are stored in row-major order (values from a whole row are stored from left to right in each row) • These rows will consists of strings of repeated grayscale values 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 84
The Science of Digital Media Compression Methods • Run-Length Encoding (RLE) – How RLE Works? • RLE uses more concise way to store repeating grayscale values as number pairs (c, n) instead of storing each of the 10, 000 pixel as an individual value • Assume the first 20 pixels in the 10, 000 pixel grayscale image file are: 255 255 255 242 242 238 238 238 255 255 • The RLE of this sequence will be (255, 6), (242, 4), (238, 6), (255, 4) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 85
The Science of Digital Media Compression Methods • Run-Length Encoding (RLE) – How RLE Works? • Number of bytes needed to store the RLE encoded version of this line of pixels is: 20 pixel x 1 byte/pixel = 20 bytes • The formula for figuring out how many bytes you need to represent a number that can be anywhere between 0 and r is: Where r is the largest run of colors, run is a continuous sequence of the same color b is the number of bytes 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 86
The Science of Digital Media Compression Methods • Run-Length Encoding (RLE) – How RLE Works? • RLE is a simple algorithm that gives acceptable results on some types of images with no risk of loss of quality • The file will still have precisely the same values for the pixels after the file is encoded and decoded • The encoded values are just represented in a way that is potentially more concise • Lossless compression algorithms are applied in situation where loss of data cannot be tolerated – gzip and copress (On the Unix platform), – pkzip and winzip (on the Windows platform) are example of tools that employ lossless compression algorithm 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 87
The Science of Digital Media Compression Methods • Run-Length Encoding (RLE) – How RLE Works? • RLE is not very effective for sound files • Image file format that offer lossless compression such as LZW are PNG and TIFF • Lossless compression can also be used as one step in a more complex algorithm that does include lossy steps, e. g. , Huffman encoding in one step during the JPEG compression algorithm 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 88
The Science of Digital Media Compression Methods • Entropy Encoding – Claude Shanno’s work in information theory sheds light on the limits of lossless compression and methods for achieving better compression rates with entropy encoding – Entropy encoding works by means of variable-length codes • Using fewer bits to encode symbols that occur more frequently while using more bits for symbols that occur infrequently 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 89
The Science of Digital Media Compression Methods • Entropy Encoding – Shannon’s equation gives estimation of whether the choice of numbers of bits for different symbols is close to optimal – The term entropy is borrowed from Physics, Shannon defines the entropy of an information source S as follows: Where S a string of symbols Is the frequency of the ith symbol in the string Can equivalently be defined as the probability that the ith symbol will appear at a given position in the string 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 90
The Science of Digital Media Compression Methods • Entropy Encoding – Example: Take an image which has exactly 256 pixels in it each pixel of different color, then frequency of each color is 1/256 – Using Shannon’s equation, the average number of bits needed to encode each color is 8 – For images with many instances of some colors, but only a few instances of others, refer to the book 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 91
The Science of Digital Media Compression Methods • Entropy Encoding – For images with many instances of some colors, but only a few instances of others, see the table below Color Frequency black white yellow orange red purple blue Green 100 20 5 5 3 20 3 – The Shannon equation becomes 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 92
The Science of Digital Media Compression Methods • Entropy Encoding – How Shannon’s Equation is applied to compression • Consider every term in the equation above individually, the first term for black and the third term for Yellow • The implication is that, those numbers are the optimum bits to encode that specific color information content in the image file • But the overall minimum value for the average number of bits required to represent each symbol-instance in this file is 2. 006 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 93
The Science of Digital Media Compression Methods • Entropy Encoding – Shannon-Fano Algorithm • Describes one way that Shannon’s equation can be applied for compression • It attempts to approach an optimum compression ratio by assigning relatively shorter code words to symbols that are used infrequently, and vice versa 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 94
The Science of Digital Media Compression Methods • Arithmetic Encoding – One drawback of Shannon-Fano Algorithm is that the optimum encoding is not possible because of the use of integer number of bits for each code – Arithmetic encoding overcomes that because it is based on statistical analysis of frequency of symbols in a file • It encodes an entire file than (or string of symbols) as one entity rather than creating a code symbol by symbol • String is symbols is encoded in a single floating point number (makes it closer to optimal than Huffman encoding) • It can be applied as one step in JPEG compression of photographic images • IBM and other companies hold patent on algorithm for arithmetic encoding 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 95
The Science of Digital Media Compression Methods • Transform Encoding – Is a lossy method (information lost is relatively unimportant) – The data is first transformed from one way of presenting to another • Discrete Cosine Tranform (DCT) • Discrete Fourier Transform (DFT) – No information is lost in the DCT or DFT – When DCT or DFT is used as one step in compression algorithm, then it becomes possible to discard redundant or irrelevant information in later steps – Hence reduction of the digital file size 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 96
The Science of Digital Media Compression Methods • Transform Encoding – The DCT • Applied to digital images to change their representation from the spatial to the frequency domain • The transformation from spatial to frequency domain is the first step in image compression • Once you have separated the high frequency components of an image, you can remove them • High frequency components corresponds to quick fluctuations of color in a short space, changes that aren’t easy for human to see • This is the basis of JPEG compression 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 97
The Science of Digital Media Compression Methods • Transform Encoding – The DFT • Applied to sound • Transforming audio data from the temporal to the frequency domain • With the frequency component separated out, it is possible to determine which frequency mask or block out other ones and then discard the masked frequencies • By this method, transform encoding is followed by perceptual encoding • The result is the smaller audio file 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 98
The Science of Digital Media Compression Methods • Compression Standards and Codecs – Some compression methods are used with each other to achieve final compressed product, e. g. , JPEG and MPEG compression requires DCT, run-length encoding and Huffman encoding – Some algorithms are standardized by official committees so that the various implementations all produce files in the same format – Patented algorithms • Commercial companies must pay a license fee to implement and sell it in a commercial product 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 99
The Science of Digital Media Compression Methods • Compression Standards and Codecs – Two prominent examples of standardized compression algorithms are: • DV for camcorder-generated digital video • Family of MPEG algorithms – Example of patented image compression algorithm • Arithmetic encoding – Codecs short for compression/decompression • Are specific implementation of compression algorithm • The word Codec is reserved for audio/video compression (as opposed to still images) • Since real-time decompression is just as important as initial compression with these time-based media 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 100
The Science of Digital Media Compression Methods • Compression Standards and Codecs – Some codecs are offered as shareware or freeware – Most codecs are commercial products – Codecs can be embedded in image, audio or video processing program, or can be sold and used seperately – Sorenson is an example of codec that is embedded in other environments (e. g. , Quick. Time) also available in professional -grade version that runs apart from other application programs – The professional-grade Sorenson compressor is actually a suite of codecs that includes implementations of MPEG and DV compression and the standard Sorenson codec 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 101
The Science of Digital Media Compression Methods • Compression Standards and Codecs – With most codecs the compression rate is adjusted by the user in accordance with the desired quality, up to maximum compression ability of the codec – Compression using bits • Bit rate and compression are inversely related • Increasing the compression rate reduces the bit rate • If there are fewer bits after the data has been compressed, then fewer bits needs to be transferred per second to play the sound or video in real time • CD-ROM will favour a lower bit rate than DVD player 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 102
The Science of Digital Media Compression Methods • Standards and Standardization Organisations for Digital Media – Three main types of standards • Proprietary • de facto • Official – Proprietary Standard • Are set and patented by commercial companies • The patents of LWZ (Lempel, Zev and Welch) compression and arithmetic encoding are examples of proprietary standards 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 103
The Science of Digital Media Compression Methods • Standards and Standardization Organisations for Digital Media – de facto Standard • Is used to describe a method or format that has become the accepted way of doing things in the industry without any official endorsement • Example TIFF files are considered by many to be the de facto standard for image files • Nearly all the image processing programs and operating systems are equipped to handle TIFF files 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 104
The Science of Digital Media Compression Methods • Standards and Standardization Organisations for Digital Media – Official Standard • Are developed by large industry consortia and/or government agencies • The organisation can exist on either a national or an international level • The main international standardization bodies are: – International Telecommunication Union (ITU) – International Organisation for Standards (ISO) – International Electrotechnical Commission (IEC) 10 February 2010 Metropolia University of Applied Sciences, Digital Media, Erkki Rämö, Principal Lecturer 105
Скачать презентацию The Science of Digital Media Course Book Details
2d0a30cdcc1ad3bd174b6189facd9a14.ppt