1 / 74

Digital Video

Digital Video. Digital video is basically a sequence of digital images Processing of digital video has much in common with digital image processing First we review the basic principles of analog television. Television Fundamentals.

bruce-ellis
Download Presentation

Digital Video

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Digital Video • Digital video is basically a sequence of digital images • Processing of digital video has much in common with digital image processing • First we review the basic principles of analog television

  2. Television Fundamentals • Color television cameras and television receivers use the RGB (red, green, blue) color system to create any color • We have seen how raster scan devices operate • Commercial television systems, however, use interlaced scanning as opposed to the progressive scanning of computer monitors

  3. Television Fundamentals • In interlaced scanning, one half of the horizontal scan lines (every other line) are transmitted and “drawn” by the receiver • Then the other half of the lines are transmitted and are drawn in between the first scan lines • Each half is known as a field, and two fields together are known as a frame

  4. Television Fundamentals • Since the phosphors retain their values for longer than the time that it takes to transmit two fields, and since rate of transmission of a field is shorter than the human eye can perceive, the viewer does not perceive this interlacing • If the frame rate is at least 25-30 frames per second the viewer does not perceive motion in an image sequence as discrete, but as continuous

  5. Interlaced Scanning • In the figures, the first field is transmitted at time t = 0 and displayed at time t = f / 2 • f is the frame rate • The second field is transmitted at time t = f / 2 and displayed at time t = f • Note that the display at time t = f consists of information (scan lines) from two distinct points in time

  6. The first field of a television transmission

  7. The second field of a television transmission

  8. A complete frame

  9. Interlacing • Interlaced scanning is used in commercial television systems to decrease the bandwidth of the transmitted signal and to reduce the phenomenon known as large area flicker • These problems had been overcome by the time bit-mapped computer monitors were being developed

  10. Deinterlacing • There are several common operations that you might want to perform on interlaced video • Producing stills • resizing the video • changing the frame etc. • Performing these operations on raw, interlaced, video can produce undesirable artifacts

  11. Interlacing Artifact

  12. Deinterlacing • Deinterlacing provides a way around these problems • All deinterlacing methods involve turning the field-based image into a frame-based image by modifying one of the fields in the image • Popular methods include duplication and interpolation

  13. Deinterlacing • Duplication • Interpolation

  14. Television Systems • The exact frame rate depends on the system as does the number of scan lines per frame • There are currently three conventional commercial television systems in use throughout the world • North America, South America and Japan use NTSC • The United Kingdom, Western Europe, Africa and Australia use PAL • France, Eastern Europe and Russia use the SECAM

  15. Television Systems

  16. Digital Television • Considering the bandwidth of the NTSC signal, how would digital transmission compare to today’s analog? We have: • 30 frames/second x 130,000 pixels/frame x 24 bits/pixel = 93.6 Mbits/second • To be competitive with analog transmission, a data compression of more than 20:1 is required • All digital television standards therefore include some form of compression. • The disadvantage of digital television therefore, is the extra bandwidth required

  17. Digital Television • The real advantage may be seen by examining the signal-to-noise ratio of digital vs. analog television • This figure shows the approximate ratio of error rate to signal-to-noise ratio for digital transmission

  18. Digital Television • An error rate of 10-8 or one bit in 100 million bits is practically undetectable • Channel error rates of 10-5 still permit acceptable pictures, especially if error correction techniques are used • An analog TV signal requires a channel with a signal-to-error ratio (SER) of 55dB

  19. Digital Television • If we use PCM for a digital television signal, the principal source of error is due to quantization • The error is a maximum of + or - 1/2 the least significant bit • For a quantization level of 8 bits, this is + or - 0.2% • This “fine” quantization would appear as white noise if viewed as a picture

  20. Digital Television • Theoretically, the SER with 8 bits is 59 dB and for each 1 bit reduction in quantization, the SER is reduced 6 dB • The actual SER of a composite color TV signal is about 4 dB less • Thus, 8-bit PCM encoding of a noise-free NTSC composite color signal yields a SER of 55 dB

  21. Digital Television • A bit error rate of 10-8 is practically undetectable • From the figure above, this requires a SER of only 21 dB • If we use the rate 10-5 with error correction bits added, a SER of 18 dB may be sufficient • This requires less than 1 bit/pixel • The essential problem in digital TV coding is therefore to reduce the picture bandwidth at the expense of the bit error rate and retain acceptable picture quality

  22. Aspect Ratios • Each of the systems listed above has an aspect ratio (ratio of width to height) of 4:3 • Cinematic films and high-definition television (HDTV) systems have aspect ratios of approximately 16:9

  23. Aspect Ratios • 4:3 aspect ratio • 16:9 aspect ratio

  24. Compatible Color TV • In order to permit compatibility of color TV transmission with preexisting black and white receivers, the RGB image generated by a television camera is converted to a YIQ image by using the transform

  25. Compatible Color TV

  26. Compatible Color TV • See book notes for more info on YIQ • The bandwidth allocated to a black and white television signal is illustrated on the next slide

  27. Compatible Color TV

  28. Compatible Color TV • In order to maintain compatibility, the color TV signal has to fit in the same bandwidth • This is accomplished by first combining the I and Q signals using a method called quadrature modulation • The two signals are multiplied by a sine and cosine function, respectively, added and become a single composite signal • The second idea is to choose the color subcarrier to be an odd multiple of one half the line frequency • The resulting bandwidth allocation is illustrated on the next slide

  29. Compatible Color TV

  30. Compatible Color TV • At the receiver, the inverse transformation given on the next slide reforms R, G, B from the received Y’, I’, Q’ signals

  31. Compatible Color TV

  32. Pixel Aspect Ratio • When we are displaying a digital video stream on a standard television receiver, we have another parameter to consider - the pixel aspect ratio • This is related to the aspect ratio of the television screen and to the sampling rate

  33. Pixel Aspect Ratio • If we have a screen with an aspect ratio of 4:3 and we have a digital image of size 711x487, then in order to maintain the 4:3 aspect ratio we must have a pixel aspect ratio p, where p can be found as follows. • 3/4 = 487/711 * p • p = 0.75 * 711/487 = 1.0950 • Computer monitors generally have pixel ratios of 1.0 (square pixels)

  34. Aspect Ratio Conversion • We are given a video sequence with an aspect ratio of 16:9 and we want to display the sequence on a device with an aspect ratio of 4:3 • For example, the source image may be 640x360 and the display device may have a resolution of 480x360 • We have several alternatives

  35. Aspect Ratio Conversion • In the letterbox technique, we scale the source image by the same amount in both the vertical and horizontal directions: 480/640=.75 • First, we create a 480x270 image: • S2[i,j] = S1[INT(4/3*i),INT(4/3*j)] • where S1 is the original image and INT is a function which rounds a floating point value to the nearest whole number

  36. Aspect Ratio Conversion • We now use this image to form the target 480x360 image S3 as follows: • S3[i,j] = S2[i,j-45] for 45≤j≤314 • S3[i,j] = 0 for 0≤j≤44, 315≤j≤359 • The second line gives the black bars of the letterbox format • In horizontal compression, we map the source image so that it exactly fills screen of the target device • This results in a stretching of the source image in the vertical direction • The transformation is: • S2[i,j] = S1[INT(3/4*i),j]

  37. Aspect Ratio Conversion • The crop and pan transformation is different from the previous two in that the transformation varies over time, depending on the contents of the source image • We will basically be showing one of the three transformed images: • S2[i,j] = S1[i+80,j] • S3[i,j] = S1[i,j] • S4[i,j] = S1[i+160,j]

  38. Aspect Ratio Conversion • S2 is the center 3/4 of the source image, S3 is the left 3/4 of the source image and S4 is the right 3/4 of the source image • If there is an object of interest in the leftmost 1/4 of the source image, we use the transformation S3 • if there is an object of interest in the rightmost 1/4 of the source image, we use S4 • otherwise, we use S2 • We view the original video sequence to determine when we need to focus on the corners of the source image, and we define the transformation accordingly

  39. Aspect Ratio Conversion • The use of only these three transformations will lead to jerkiness when we shift from one transform to another • We can get the effect of a camera panning by making a time-dependent transformation • For example, imagine we want to pan from the center view to the right view over some period • We can define the transformation as follows: • S5[i,j,t] = S1[i+80+INT(80*(t-t1)/(t2-t1)),j,t], t1≤t≤t2 assuming that there is no parallel change of frame rate

  40. Sample rate conversion • At times, it will be necessary to convert the sampling rate in a source signal to some other sampling rate • Consider converting from a CCIR 601 signal (a digital video standard) to an MPEG SIF signal • MPEG is a compression standard for video • SIF is the source input format for the compression) • CCIR 601 is an interlaced signal with a 50 Hz field rate • The signal consists of three components: Y, U and V

  41. Sample rate conversion • The Y component (luminance) is sampled at a resolution of 720x480 • The U and V components (chrominance) are sampled at a resolution of 360x480 • The sampling pattern is shown on the next slide

  42. CCIR 601 Sampling Pattern

  43. Sample rate conversion • MPEG SIF samples luminance at a resolution of 360x240 and chrominance at a resolution of 180x120 • The sampling pattern is different as well, as shown on the next slide

  44. MPEG SIF Sampling Pattern

  45. Sample rate conversion • The conversion to the lower sampling resolution begins with the discarding of one of the interlaced fields • This reduces the picture rate to 25 Hz (non-interlaced) and reduces the vertical resolution by one half

  46. Sample rate conversion • Now, the luminance is decimated by one half in the horizontal direction • One possibility is to subsample the values, however better results are obtained by applying an FIR filter before subsampling • One filter which has been found to give good results in decimating the luminance is shown on the following slide

  47. Luminance subsampling filter weights

  48. Sample rate conversion • The results of multiplying the filter weights by the original values is divided by 256 • Use of a power of two allows a simple hardware implmentation • An example of the use of this filter followed by subsampling is shown on the next slide • At the ends of lines, some special technique such as renormalizing the filter or replicating the last pixel must be used • In the example below, the data in the line is reflected at each end

  49. Example of filtering and subsampling of a line of pixels 10 12 20 30 35 15 19 11 11 19 26 45 80 90 92 90 32 32 23 9 12 49 95 95

More Related