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Digital Media. Dr. Jim Rowan ITEC 2110 Video Part 2. Coping with Video Size. Consider human vision limitations 1) Chrominance sub-sampling Compression - two versions 2) Spatial 3) Temporal differencing vectoring. Coping with Video Size. Consider human vision limitations

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digital media

Digital Media

Dr. Jim Rowan

ITEC 2110

Video Part 2

coping with video size
Coping with Video Size

Consider human vision limitations

1) Chrominance sub-sampling

Compression - two versions

2) Spatial

3) Temporal

differencing

vectoring

coping with video size1
Coping with Video Size

Consider human vision limitations

1) Chrominance sub-sampling

Compression - two versions

3) spatial

4) Temporal

differencing

vectoring

chrominance sub sampling
Chrominance sub-sampling
  • Humans can’t distinguish changes in color as well as they can distinguish luminance changes
    • http://en.wikipedia.org/wiki/Chroma_subsampling
  • In our cameras…
    • Of every 4 frames
    • store the luminance for each frame
    • only store a proportion of the color info
    • 4:2:0
slide5

Chrominance sub-sampling

http://dougkerr.net/pumpkin/articles/Subsampling.pdf

slide6

Chrominance sub-sampling

Luminance, Cr, Cb

http://en.wikipedia.org/wiki/Chroma_subsampling#Sampling_systems_and_ratios

coping with video size2
Coping with Video Size
  • Aside from screen size and frame rate...
  • Consider human vision limitations

1) Chrominance sub-sampling

  • Compression - two versions

2) spatial

3) Temporal

differencing

vectoring

coping with video size3
Coping with Video Size
  • Spatial compression
      • Individual images can be compressed using the techniques discussed in the bitmapped section
      • Doesn’t result in very much compression for video
      • Doesn’t take into consideration the other frames that come before or after it
      • Our video cameras do this, compressing each frame to jpeg
coping with video size4
Coping with Video Size
  • Aside from screen size and frame rate...
  • Consider human vision limitations

1) Chrominance sub-sampling

  • Compression - two versions

2) spatial

3) Temporal

differencing

vectoring

temporal compression differencing
Temporal Compression differencing
  • Use the Difference in two frames
    • A naive approach can result in good compression
    • Works well for a small amount of movement
      • Security cameras spend most of their time “seeing” the same thing all night long
    • A Tarantino film? not so much…
      • Most pixels change with nearly every frame

Saved

Frame 2

Saved

Frame 1

Captured

Frame 1

Captured

Frame 2

image differencing
Image Differencing
  • To subtract one image from the next
  • Do it one pixel at a time
    • red minus red
    • green minus green
    • blue minus blue
  • Store the difference
  • To play it back
    • Play frame one 1
    • Add frame 2 to frame 1
  • Next, an example in black & white
temporal compression vectoring
Temporal Compressionvectoring
  • When an OBJECT moves
    • compute its trajectory
    • fill in the resulting exposed background

Captured

Frame 1

Captured

Frame 2

Stored

Background

Stored

Object

Movement

Vector

  • BUT there’s a problem...
  • why isn’t this an easy thing to do?
more on differencing
More on differencing
  • The differencing can happen in a forward manner and a backward manner
  • It might be more economical (in data size) to create a frame from a frame that follows it...
slide18

MPEG-2

iFrame

pFrame

bFrame

GOP

http://en.wikipedia.org/wiki/Inter_frame

http://en.wikipedia.org/wiki/I-frames#Intra_coded_frames_.28or_slices_or_I-frames_or_Key_frames.29

slide19

iFrame:

-a keyframe

-spatially compressed

(a fully specified image)

pFrame

-predicted frame

-contains only the difference

between the current frame and

the previous iFrame

(smaller in size than iFrame)

bFrame

-bi-predicted frame

-contains difference between

current and both the preceding

and following iFrames

-even smaller in size than iFrame

slide20

3 mpeg 2 video streams

http://www.spiritalchemy.com/blog/wp-content/uploads/2009/10/IPB_frame_example.jpg

Group(s) Of Pictures (GOPs)

The largest

All spatially (intra-frame)

compressed

Spatially compressed

and predictive

(difference)

Smaller…

but more

computation

Spatially compressed forward and backward predictive

(difference)

Smallest…

but more

computation

and it is

transported

out of order!

Play sequence: 1 2 3 4 5 6 7 8 9 10

Transmit sequence: 1 4 2 3 7 5 6 10 8 9

slide21

Mpeg compression technique 1

Mpeg compression technique 2

Mpeg compression technique 3

Frame capture sequence: 1 2 3 4 5 6 7 8 9 10

slide22
So…

How does this ===>

happen?

mpeg encoding
Mpeg encoding

http://www.100fps.com/codec_quality_comparison.htm

  • It’s COMPLICATED…
  • Images are broken up and transmitted as macroblocks
  • They can be a variety of sizes
    • Typically 8X8 or 16X16
  • When missing (or there are video errors)
    • May use a lower resolution block (that was previously received)
    • May just use a solid color block instead
video compression what does this
Video CompressionWhat does this?
  • http://en.wikipedia.org/wiki/Video_codec
  • Coder/Decoder - Codec
    • encodes and decodes video
  • Can be symmetric
      • it takes as long to compress as decompress
  • Can be asymmetric
      • it takes longer to compress or decompress than it does to decompress to compress
slide26

Video Compression

Can be very complex

Each image is just jpeg compressed

Each image is jpeg compressed; the p-frames

are the result of frames being subtracted from

the i-frames

Each image is jpeg compressed; the p-frames

are the result of frames being subtracted from

the i-frames; b-frames are the result of

frames being subtracted from i-frames and

future p-frames

a final worry
A final worry...
  • Codecs compress video to make it smaller
  • There are a variety of codecs to do this
  • Which to choose?
    • It is a tradeoff between compression technique, its computational complexity and its artifacts
so how do codecs vary
So... How do codecs vary?
  • compression and decompression complexity
    • affects the artifacts that are created
    • affects the time required to carry them out
    • affects the volume of the data stream created
    • affects the type and expense of the equipment used
    • affects whether or not it can be implemented in hardware of software
  • WMV, DivX, Cinepak, Intel Indeo & Sorenson
slide29

Which is the original?

Notice the artifacts?

http://www.100fps.com/codec_quality_comparison.htm

slide30

Center one is the original

Left is “sharpened” Right is “blurred”

http://www.100fps.com/codec_quality_comparison.htm